{"id":13688321,"url":"https://github.com/lixus7/Time-Series-Works-Conferences","last_synced_at":"2025-05-01T16:31:37.547Z","repository":{"id":39346135,"uuid":"473491411","full_name":"lixus7/Time-Series-Works-Conferences","owner":"lixus7","description":"Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)","archived":false,"fork":false,"pushed_at":"2025-03-06T06:18:59.000Z","size":4336,"stargazers_count":869,"open_issues_count":0,"forks_count":92,"subscribers_count":23,"default_branch":"main","last_synced_at":"2025-03-06T07:35:07.264Z","etag":null,"topics":["accident-detection","anomaly-detection","deep-learning","demand-forecasting","location","multivariate-timeseries","paper-list","probabilistic-models","spatio-temporal","spatio-temporal-data","spatio-temporal-modeling","spatio-temporal-prediction","time-series","time-series-forecasting","time-series-imputation","time-series-prediction","traffic-prediction","travel-time-prediction"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lixus7.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"license","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-03-24T06:59:12.000Z","updated_at":"2025-03-06T06:19:02.000Z","dependencies_parsed_at":"2023-10-27T09:35:06.291Z","dependency_job_id":"e0f3c642-c90c-41dd-b865-099103c25dae","html_url":"https://github.com/lixus7/Time-Series-Works-Conferences","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lixus7%2FTime-Series-Works-Conferences","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lixus7%2FTime-Series-Works-Conferences/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lixus7%2FTime-Series-Works-Conferences/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lixus7%2FTime-Series-Works-Conferences/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lixus7","download_url":"https://codeload.github.com/lixus7/Time-Series-Works-Conferences/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251906893,"owners_count":21663178,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["accident-detection","anomaly-detection","deep-learning","demand-forecasting","location","multivariate-timeseries","paper-list","probabilistic-models","spatio-temporal","spatio-temporal-data","spatio-temporal-modeling","spatio-temporal-prediction","time-series","time-series-forecasting","time-series-imputation","time-series-prediction","traffic-prediction","travel-time-prediction"],"created_at":"2024-08-02T15:01:11.367Z","updated_at":"2025-05-01T16:31:37.525Z","avatar_url":"https://github.com/lixus7.png","language":null,"funding_links":[],"categories":["Others"],"sub_categories":[],"readme":"# Time-Series Works and Conferences\n\n# Backlog (To do): KDD 2025, ...\n\n**Visit our [GitHub Page](https://lixus7.github.io/Time-Series-Works-Conferences/) for a better view.**\n\n\n\u003ca href=\"#Conferences\"\u003eClick here to jump to the Conferences page with more conference information.\u003c/a\u003e\n\nor [AI ML Summary Github](https://github.com/Lionelsy/Conference-Accepted-Paper-List)\n\nSome other nice time-series repositories:\n\n[xiyuanzh/time-series-papers](https://github.com/xiyuanzh/time-series-papers)\n\n[qingsongedu/awesome-AI-for-time-series-papers](https://github.com/qingsongedu/awesome-AI-for-time-series-papers)\n\n[xuehaouwa/Awesome-Trajectory-Prediction](https://github.com/xuehaouwa/Awesome-Trajectory-Prediction)\n\n[My Time-series Repo-Star List](https://github.com/stars/lixus7/lists/time-series-list)\n\n\u003cdiv align=\"center\"\u003e\n\u003c!-- \u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/54fdbe8888c0a75717d7939b42f3d744b77483b0/687474703a2f2f6a617977636a6c6f76652e6769746875622e696f2f73622f69636f2f617765736f6d652e737667\" /\u003e\n\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/1ef04f27611ff643eb57eb87cc0f1204d7a6a14d/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d254630253946253843253946266d6573736167653d496625323055736566756c267374796c653d7374796c653d666c617426636f6c6f723d424334453939\" /\u003e\n\u003ca href=\"https://github.com/lixus7\"\u003e     \u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/41e8e16b771d56dd768f7055354613254961d169/687474703a2f2f6a617977636a6c6f76652e6769746875622e696f2f73622f6769746875622f677265656e2d666f6c6c6f772e737667\" /\u003e \u003c/a\u003e --\u003e\n\u003ca href=\"https://github.com/lixus7/Time-Series-Works-Conferences/issues\"\u003e     \u003cimg border=\"0\" src=\"https://img.shields.io/github/issues/lixus7/Time-Series-Works-Conferences\" /\u003e \u003c/a\u003e\n\u003ca href=\"https://github.com/lixus7/Time-Series-Works-Conferences/network/members\"\u003e     \u003cimg border=\"0\" src=\"https://img.shields.io/github/forks/lixus7/Time-Series-Works-Conferences\" /\u003e \u003c/a\u003e\n\u003ca href=\"https://github.com/lixus7/Time-Series-Works-Conferences/stargazers\"\u003e     \u003cimg border=\"0\" src=\"https://img.shields.io/github/stars/lixus7/Time-Series-Works-Conferences\" /\u003e \u003c/a\u003e\n\u003c!-- \u003ca href=\"https://github.com/lixus7/Time-Series-Works-Conferences/blob/main/docs/img/WeChat.jpeg\"\u003e     \u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/013c283843363c72b1463af208803bfbd5746292/687474703a2f2f6a617977636a6c6f76652e6769746875622e696f2f73622f69636f2f7765636861742e737667\" /\u003e \u003c/a\u003e --\u003e\n\u003c/div\u003e\n\n\n\n\n\u003e I have a strong interest in time-series research. Welcome to contact me for discussions and collaborative efforts.\n\u003cbr\u003e I am currently pursuing a doctoral degree in CSE of UNSW, Sydney, under the supervision of Prof. [Flora Salim](https://scholar.google.com.hk/citations?user=Yz35RSYAAAAJ\u0026hl=zh-CN\u0026oi=ao) and [Hao Xue](https://scholar.google.com.hk/citations?user=KwhLl7IAAAAJ\u0026hl=zh-CN\u0026oi=ao). I got the master degree under the supervision of Prof. [Xuan Song](https://scholar.google.com.hk/citations?user=_qCSLpMAAAAJ\u0026hl=zh-CN\u0026oi=ao), [Quanjun Chen](https://scholar.google.com.hk/citations?user=_PKwzTwAAAAJ\u0026hl=zh-CN) and [Renhe Jiang](https://scholar.google.com.hk/citations?user=Yo2lwasAAAAJ\u0026hl=zh-CN\u0026oi=ao).\n\n\n\nThe task section has been completed and we will continue to update the methodology section. If you encounter any missing resources (papers/code) or errors, please don't hesitate to open an issue or make a pull request. Additionally, if you're interested in collaborating on this work, please feel free to contact me.\n\nAll papers are organized by task and methodology, including those not included in this GitHub repository, and are available for everyone to use on OneDrive and Google Drive (VPN required). \n\n[OneDrive](https://1drv.ms/u/s!Au2cJRs-_u93lDbLrSDkDy8htv2V?e=ftuaXd)\n \n[Google Drive](https://drive.google.com/drive/folders/17bILWdDxUrufRp3yilYfoU5VKywwS1g6?usp=sharing)\n\n\n\n\nTo reduce repetition, some data are in abbreviated form. Some terms may not represent general interpretations and apply only to this repository.\n\n|Full Name | Abbreviation|\n|:--|:--|\n| Adaptive GNN                       |  AGNN   |\n| Attention                          |  Attn   |  \n| AutoRegression(RNN,GRU,LSTM)       |  AR     |\n| Controlled Differential Equations  |  CDE    |  \n| Contrastive Learning               |  CL     |\n| Encoder Decoder                    |  EncDec |  \n| Ensemble                           |  Ens    |\n| Feature Decomposed                 |  FeaD   |\n| Federated   Learning               |  FL     |  \n| Generative Adversarial Network     |  GAN    |\n|  Graph Convolutional Network       |  GCN    |   \n| Hour, Day, Week, Month, etc        |  HA     |\n| Heterogeneous GNN                  |  HGNN   |\n| Multiple Graph                     |  MGNN   |\n| Memory                             |  Mem    |   \n| Meta Learning                      |  MetaL  |   \n| MultiTask                          |  MulT   |     \n| Network Architechture Search       |  NAS    |  \n| Ordinary Differential Equations    |  ODE    |\n| Statistic                          |  Stat   |\n| TCN (WaveNet)                      |  TCN    |   \n| Temporal Graph Network             |  TGN    |   \n| Transformer                        |  Trans  |  \n| Transfer Learning                  |  TransL |    \n| Variational Auto-Encoder           |  VAE    |\n\n# Recent Time Series Works Grouped by Task\n\n- \u003ca href = \"#Multivariable-Time-Series-Forecasting\"\u003eMultivariable Time Series Forecasting\u003c/a\u003e\n- \u003ca href = \"#Multivariable-Probabilistic-Time-Series-Forecasting\"\u003eMultivariable Probabilistic Time Series Forecasting\u003c/a\u003e\n- \u003ca href = \"#Time-Series-Imputation\"\u003eTime Series Imputation\u003c/a\u003e\n- \u003ca href = \"#Time-Series-Anomaly-Detection\"\u003eTime Series Anomaly Detection\u003c/a\u003e\n- \u003ca href = \"#Demand-Prediction\"\u003eDemand Prediction\u003c/a\u003e\n- \u003ca href = \"#Time-Series-Generation\"\u003eTime Series Generation\u003c/a\u003e\n- \u003ca href = \"#Travel-Time-Estimation\"\u003eTravel Time Estimation\u003c/a\u003e\n- \u003ca href = \"#Traffic-Location-Prediction\"\u003eTraffic Location Prediction\u003c/a\u003e\n- \u003ca href = \"#Event-Prediction\"\u003eEvent Prediction\u003c/a\u003e\n- \u003ca href = \"#Stock-Prediction\"\u003eStock Prediction\u003c/a\u003e\n- \u003ca href = \"#Other-Forecasting\"\u003eOther Forecasting\u003c/a\u003e\n\n  \n\n# [Multivariable Time Series Forecasting](#content)\n|  Task  |    Data |   Model  | Paper   |    Code    |   Publication    |\n| :-: | :-: | :-: | :-: | :-: | - |\n| Paper Nums:100+ | \u003cimg width=150/\u003e | \u003cimg width=220/\u003e  |   |   |   \u003cimg width=300/\u003e |\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    SSCNN | [Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting](https://proceedings.neurips.cc/paper_files/paper/2024/hash/7b122d0a0dcb1a86ffa25ccba154652b-Abstract-Conference.html) | [Pytorch](https://github.com/JLDeng/SSCNN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/JLDeng/SSCNN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/JLDeng/SSCNN?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |     | [Are Language Models Actually Useful for Time Series Forecasting?](https://openreview.net/forum?id=54NSHO0lFe) | [Pytorch](https://github.com/BennyTMT/LLMsForTimeSeries)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/BennyTMT/LLMsForTimeSeries?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/BennyTMT/LLMsForTimeSeries?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    PGN | [PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting](https://openreview.net/forum?id=ypEamFKu2O\u0026noteId=jpzTU4OIxe) | [Pytorch](https://github.com/Water2sea/TPGN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Water2sea/TPGN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Water2sea/TPGN?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    CATS | [Are Self-Attentions Effective for Time Series Forecasting?](https://openreview.net/forum?id=iN43sJoib7\u0026noteId=VrwF0T4VGH) | [Pytorch](https://github.com/dongbeank/CATS)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/dongbeank/CATS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/dongbeank/CATS?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    Attraos | [Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective](https://openreview.net/forum?id=fEYHZzN7kX) | [Pytorch](https://github.com/CityMind-Lab/NeurIPS24-Attraos)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/CityMind-Lab/NeurIPS24-Attraos?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/CityMind-Lab/NeurIPS24-Attraos?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    Time-FFM | [Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting](https://openreview.net/forum?id=HS0faHRhWD) | [Pytorch](https://github.com/yuppielqx/Time-FFM)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/yuppielqx/Time-FFM?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/yuppielqx/Time-FFM?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    Chimera | [Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models](https://openreview.net/forum?id=ncYGjx2vnE) | [Pytorch](https://github.com/ABehrouz/Chimera)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/ABehrouz/Chimera?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/ABehrouz/Chimera?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    TimeXer | [TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables](https://openreview.net/forum?id=INAeUQ04lT) | [Pytorch](https://github.com/thuml/TimeXer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/thuml/TimeXer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/thuml/TimeXer?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    MiTSformer| [Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment](https://openreview.net/forum?id=EMV8nIDZJn) | [Pytorch](https://github.com/chunhuiz/MiTSformer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/chunhuiz/MiTSformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/chunhuiz/MiTSformer?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    TTMs| [Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series](https://openreview.net/forum?id=3O5YCEWETq) | [Pytorch](https://github.com/ibm-granite/granite-tsfm)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/ibm-granite/granite-tsfm?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/ibm-granite/granite-tsfm?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    Sumba| [Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics](https://openreview.net/forum?id=co7DsOwcop) | [Pytorch](https://github.com/chenxiaodanhit/Sumba)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/chenxiaodanhit/Sumba?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/chenxiaodanhit/Sumba?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    Peri-midFormer | [Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis](https://openreview.net/forum?id=5iUxMVJVEV) | [Pytorch](https://github.com/WuQiangXDU/Peri-midFormer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/WuQiangXDU/Peri-midFormer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/WuQiangXDU/Peri-midFormer?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    Ada-MSHyper | [Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting](https://openreview.net/forum?id=RNbrIQ0se8) | [Pytorch](https://github.com/shangzongjiang/Ada-MSHyper)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/shangzongjiang/Ada-MSHyper?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/shangzongjiang/Ada-MSHyper?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable | ...  |    LPTM | [Large Pre-trained time series models for cross-domain Time series analysis tasks](https://openreview.net/forum?id=vMMzjCr5Zj) | [Pytorch](https://github.com/AdityaLab/LPTM)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/AdityaLab/LPTM?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/AdityaLab/LPTM?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    CCM | [From Similarity to Superiority: Channel Clustering for Time Series Forecasting](https://openreview.net/forum?id=MDgn9aazo0) | [Pytorch](https://github.com/Graph-and-Geometric-Learning/TimeSeriesCCM)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Graph-and-Geometric-Learning/TimeSeriesCCM?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Graph-and-Geometric-Learning/TimeSeriesCCM?color=critical\u0026style=social)  | NIPS 2024\n| Add News |  Electricity \u003cbr\u003e Exchange  \u003cbr\u003e Traffic \u003cbr\u003e Bitcoin  |     | [From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection](https://openreview.net/forum?id=DpByqSbdhI) | [Pytorch](https://github.com/ameliawong1996/From_News_to_Forecast)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/ameliawong1996/From_News_to_Forecast?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/ameliawong1996/From_News_to_Forecast?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  ImageBind \u003cbr\u003e IMU2CLIP  \u003cbr\u003e IMUGPT \u003cbr\u003e HARGPT \u003cbr\u003e LLaVA |    UniMTS | [UniMTS: Unified Pre-training for Motion Time Series](https://openreview.net/forum?id=DpByqSbdhI) | [Pytorch](https://github.com/xiyuanzh/UniMTS)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/xiyuanzh/UniMTS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/xiyuanzh/UniMTS?color=critical\u0026style=social)  | NIPS 2024\n| Attack |  PEMS03 \u003cbr\u003e PEMS04  \u003cbr\u003e PEMS08 \u003cbr\u003e Weather \u003cbr\u003e ETTm1 |    BackTime | [BackTime: Backdoor Attacks on Multivariate Time Series Forecasting](https://openreview.net/forum?id=y8HUXkwAOg) | [Pytorch](https://github.com/xiaolin-cs/BackTime)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/xiaolin-cs/BackTime?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/xiaolin-cs/BackTime?color=critical\u0026style=social)  | NIPS 2024\n| Less data |  Electricity \u003cbr\u003e Solar  \u003cbr\u003e Traffic \u003cbr\u003e PEMS-BAY \u003cbr\u003e METR-LA |    ChronoEpilogi | [ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions](https://openreview.net/forum?id=y8HUXkwAOg) | [Pytorch](https://github.com/ev07/ChronoEpilogi)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/ev07/ChronoEpilogi?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/ev07/ChronoEpilogi?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |    FAN | [Frequency Adaptive Normalization For Non-stationary Time Series Forecasting](https://openreview.net/forum?id=T0axIflVDD) | [Pytorch](https://github.com/wayne155/FAN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/wayne155/FAN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/wayne155/FAN?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |    GLAFF | [Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective](https://openreview.net/forum?id=EY2agT920S) | [Pytorch](https://github.com/ForestsKing/GLAFF)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/ForestsKing/GLAFF?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/ForestsKing/GLAFF?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |    FilterNet | [FilterNet: Harnessing Frequency Filters for Time Series Forecasting](https://openreview.net/forum?id=ugL2D9idAD) | [Pytorch](https://github.com/aikunyi/FilterNet)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/aikunyi/FilterNet?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/aikunyi/FilterNet?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |    CycleNet | [CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns](https://openreview.net/forum?id=clBiQUgj4w) | [Pytorch](https://github.com/ACAT-SCUT/CycleNet)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/ACAT-SCUT/CycleNet?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/ACAT-SCUT/CycleNet?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |    RATD | [Retrieval-Augmented Diffusion Models for Time Series Forecasting](https://openreview.net/forum?id=dRJJt0Ji48\u0026noteId=8wGyyvVUNr) | [Pytorch](https://github.com/stanliu96/RATD)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/stanliu96/RATD?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/stanliu96/RATD?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |    DDN | [DDN: Dual-domain Dynamic Normalization for Non-stationary Time Series Forecasting](https://openreview.net/forum?id=RVZfra6sZo) | [Pytorch](https://github.com/Hank0626/DDN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Hank0626/DDN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Hank0626/DDN?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |    FBM | [Rethinking Fourier Transform from A Basis Functions Perspective for Long-term Time Series Forecasting](https://openreview.net/forum?id=BAfKBkr8IP) | [Pytorch](https://github.com/runze1223/Fourier-Basis-Mapping)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/runze1223/Fourier-Basis-Mapping?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/runze1223/Fourier-Basis-Mapping?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    BSA | [Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting](https://openreview.net/forum?id=dxyNVEBQMp) | [Pytorch](https://github.com/DJLee1208/BSA_2024)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/DJLee1208/BSA_2024?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/DJLee1208/BSA_2024?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    DeformableTST | [DeformableTST: Transformer for Time Series Forecasting without Over-reliance on Patching](https://openreview.net/forum?id=B1Iq1EOiVU) | [Pytorch](https://github.com/luodhhh/DeformableTST)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/luodhhh/DeformableTST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/luodhhh/DeformableTST?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    SOFTS | [SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion](https://openreview.net/forum?id=89AUi5L1uA) | [Pytorch](https://github.com/Secilia-Cxy/SOFTS)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Secilia-Cxy/SOFTS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Secilia-Cxy/SOFTS?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |     | [Scaling Law for Time Series Forecasting](https://openreview.net/forum?id=Cr2jEHJB9q) | [Pytorch](https://github.com/JingzheShi/ScalingLawForTimeSeriesForecasting)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/JingzheShi/ScalingLawForTimeSeriesForecasting?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/JingzheShi/ScalingLawForTimeSeriesForecasting?color=critical\u0026style=social)  | NIPS 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    AutoTimes | [AutoTimes: Autoregressive Time Series Forecasters via Large Language Models](https://openreview.net/forum?id=HS0faHRhWD) | [Pytorch](https://github.com/thuml/AutoTimes)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/thuml/AutoTimes?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/thuml/AutoTimes?color=critical\u0026style=social)  | NIPS 2024\n| Multi Task |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    UniTS | [UniTS: A Unified Multi-Task Time Series Model](https://openreview.net/forum?id=nBOdYBptWW) | [Pytorch](https://github.com/mims-harvard/UniTS)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/mims-harvard/UniTS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/mims-harvard/UniTS?color=critical\u0026style=social)  | NIPS 2024\n| Foundation TS | ...   |    MOMENT | [MOMENT: A Family of Open Time-series Foundation Models](https://icml.cc/virtual/2024/poster/34530) | [Pytorch](https://github.com/moment-timeseries-foundation-model/moment)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/moment-timeseries-foundation-model/moment?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/moment-timeseries-foundation-model/moment?color=critical\u0026style=social)  | ICML 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    OSL | [An Analysis of Linear Time Series Forecasting Models](https://icml.cc/virtual/2024/poster/32697) | [Pytorch](https://github.com/sir-lab/linear-forecasting)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/sir-lab/linear-forecasting?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/sir-lab/linear-forecasting?color=critical\u0026style=social)  | ICML 2024\n| Six |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    UP2ME | [UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis](https://icml.cc/virtual/2024/poster/33686) | [Pytorch](https://github.com/Thinklab-SJTU/UP2ME)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Thinklab-SJTU/UP2ME?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Thinklab-SJTU/UP2ME?color=critical\u0026style=social)  | ICML 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    SparseTSF | [SparseTSF: Modeling Long-term Time Series Forecasting with *1k* Parameters](https://openreview.net/forum?id=54NSHO0lFe) | [Pytorch](https://github.com/lss-1138/SparseTSF)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/lss-1138/SparseTSF?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/lss-1138/SparseTSF?color=critical\u0026style=social)  | ICML 2024\n| Multivariable |  Electricity  \u003cbr\u003e PEMSD7M \u003cbr\u003e BikeNYC \u003cbr\u003e [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    SCNN | [Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting](https://ieeexplore.ieee.org/document/10457027) | [Pytorch](https://github.com/JLDeng/SCNN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/JLDeng/SCNN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/JLDeng/SCNN?color=critical\u0026style=social)  | TKDE 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    iTransformer | [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://openreview.net/forum?id=JePfAI8fah) | [Pytorch](https://github.com/thuml/iTransformer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/thuml/iTransformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/thuml/iTransformer?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable | NorPool  \u003cbr\u003e Caiso  \u003cbr\u003e Traffic  \u003cbr\u003e Electricity   \u003cbr\u003e Weather  \u003cbr\u003e Exchange   \u003cbr\u003e     ETT       \u003cbr\u003e Wind  |    mr-Diff | [Multi-Resolution Diffusion Models for Time Series Forecasting](https://openreview.net/forum?id=mmjnr0G8ZY) | None  | ICLR 2024\n| Multivariable | ETT     \u003cbr\u003e Electricity   \u003cbr\u003e Weather \u003cbr\u003e Traffic  \u003cbr\u003e Exchange  \u003cbr\u003e ILI  |    ModernTCN | [Multi-Resolution Diffusion Models for Time Series Forecasting](https://openreview.net/forum?id=vpJMJerXHU) | [Pytorch](https://github.com/luodhhh/ModernTCN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/luodhhh/ModernTCN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/luodhhh/ModernTCN?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    Time-LLM | [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://openreview.net/forum?id=Unb5CVPtae) | [Pytorch](https://github.com/KimMeen/Time-LLM)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/KimMeen/Time-LLM?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/KimMeen/Time-LLM?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |   TEMPO | [TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting](https://openreview.net/forum?id=YH5w12OUuU) | [Pytorch](https://github.com/DC-research/TEMPO)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/DC-research/TEMPO?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/DC-research/TEMPO?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |   CARD | [CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting](https://openreview.net/forum?id=MJksrOhurE) | [Pytorch](https://github.com/wxie9/CARD)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/wxie9/CARD?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/wxie9/CARD?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |  ARM | [ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning](https://openreview.net/forum?id=JWpwDdVbaM) | None | ICLR 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |  DAM | [DAM: Towards a Foundation Model for Forecasting](https://openreview.net/forum?id=4NhMhElWqP) | [None](https://openreview.net/attachment?id=4NhMhElWqP\u0026name=supplementary_material) | ICLR 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)  \u003cbr\u003e PEMS3478 |  TimeMixer | [TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting](https://openreview.net/forum?id=7oLshfEIC2) | [Pytorch](https://github.com/kwuking/TimeMixer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/kwuking/TimeMixer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/kwuking/TimeMixer?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |  PDF  | [Periodicity Decoupling Framework for Long-term Series Forecasting](https://openreview.net/forum?id=dp27P5HBBt) | [Pytorch](https://github.com/Hank0626/PDF)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Hank0626/PDF?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Hank0626/PDF?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable \u003cbr\u003e Missing Value|  METR-LA  \u003cbr\u003e Electricity  \u003cbr\u003e PEMS \u003cbr\u003e ETT \u003cbr\u003e BeijingAir|  BiTGraph  | [Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values](https://openreview.net/forum?id=O9nZCwdGcG) | [Pytorch](https://github.com/chenxiaodanhit/BiTGraph)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/chenxiaodanhit/BiTGraph?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/chenxiaodanhit/BiTGraph?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)  \u003cbr\u003e PEMS08 |  LIFT  | [Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators](https://openreview.net/forum?id=JiTVtCUOpS) | [Pytorch](https://github.com/SJTU-Quant/LIFT)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/SJTU-Quant/LIFT?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/SJTU-Quant/LIFT?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable | ETT     \u003cbr\u003e Weather \u003cbr\u003e ILI  \u003cbr\u003e Traffic   |    STanHop | [STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction](https://openreview.net/forum?id=6iwg437CZs) | [Pytorch](https://github.com/MAGICS-LAB/STanHop)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/MAGICS-LAB/STanHop?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/MAGICS-LAB/STanHop?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable | ETT     \u003cbr\u003e Weather  \u003cbr\u003e Electricity  \u003cbr\u003e Traffic \u003cbr\u003e ILI    \u003cbr\u003e CloudCluster |    Pathformer | [Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting](https://openreview.net/forum?id=vpJMJerXHU) | [Pytorch](https://github.com/decisionintelligence/pathformer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/decisionintelligence/pathformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/decisionintelligence/pathformer?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable |   [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    pits | [Learning to Embed Time Series Patches Independently](https://openreview.net/forum?id=vpJMJerXHU) | [Pytorch](https://github.com/seunghan96/pits)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/seunghan96/pits?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/seunghan96/pits?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable | ETT    \u003cbr\u003e Weather  \u003cbr\u003e Electricity  \u003cbr\u003e Traffic   |    FITS | [FITS: Modeling Time Series with 10k Parameters](https://openreview.net/forum?id=bWcnvZ3qMb) | [Pytorch](https://github.com/VEWOXIC/FITS)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/VEWOXIC/FITS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/VEWOXIC/FITS?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable | ETT  \u003cbr\u003e Electricity    \u003cbr\u003e Weather  \u003cbr\u003e Lora   |    AutoTCL | [Parametric Augmentation for Time Series Contrastive Learnin](https://openreview.net/forum?id=EIPLdFy3vp) | [Pytorch](https://github.com/AslanDing/AutoTCL)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/AslanDing/AutoTCL?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/AslanDing/AutoTCL?color=critical\u0026style=social)  | ICLR 2024\n| Multivariable | ETT    \u003cbr\u003e Exchange  \u003cbr\u003e ILI   |    GLIP | [Interpretable Sparse System Identification: Beyond Recent Deep Learning Techniques on Time-Series Prediction](https://openreview.net/forum?id=aFWUY3E7ws) | [Pytorch](https://openreview.net/attachment?id=aFWUY3E7ws\u0026name=supplementary_material)   | ICLR 2024\n| Multivariable | [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |    Fredformer | [Fredformer: Frequency Debiased Transformer for Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3637528.3671855) |  [Pytorch](https://github.com/chenzRG/Fredformer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/chenzRG/Fredformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/chenzRG/Fredformer?color=critical\u0026style=social)  | KDD 2024\n| Multivariable | [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |    GPHT | [Generative Pretrained Hierarchical Transformer for Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3637528.3671855) |  [Pytorch](https://github.com/icantnamemyself/GPHT)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/icantnamemyself/GPHT?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/icantnamemyself/GPHT?color=critical\u0026style=social)  | KDD 2024\n| Multivariable | [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |    FRNet | [FRNet: Frequency-based Rotation Network for Long-term Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3637528.3671713) |  [Pytorch](https://github.com/SiriZhang45/FRNet)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/SiriZhang45/FRNet?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/SiriZhang45/FRNet?color=critical\u0026style=social)  | KDD 2024\n| Missing MTS | METR-LA \u003cbr\u003e PEMS-BAY \u003cbr\u003e PEMS04 \u003cbr\u003e PEMS08 \u003cbr\u003e China AQI   |    GinAR | [GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing](https://dl.acm.org/doi/abs/10.1145/3637528.3672055) |  [Pytorch](https://github.com/GestaltCogTeam/GinAR)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/GestaltCogTeam/GinAR?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/GestaltCogTeam/GinAR?color=critical\u0026style=social)  | KDD 2024\n| Multivariable | METR-LA \u003cbr\u003e PEMS-BAY \u003cbr\u003e PEMS04 \u003cbr\u003e \u003cbr\u003e  PEMS07  PEMS08   |    HimNet | [Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3637528.3671961) |  [Pytorch](https://github.com/XDZhelheim/HimNet)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/XDZhelheim/HimNet?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/XDZhelheim/HimNet?color=critical\u0026style=social)  | KDD 2024\n| Multivariable | [TimesNet_data](https://github.com/thuml/Time-Series-Library)  |    CDS | [Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift](https://dl.acm.org/doi/abs/10.1145/3637528.3671926) |  [Pytorch](https://github.com/HALF111/calibration_CDS)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/HALF111/calibration_CDS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/HALF111/calibration_CDS?color=critical\u0026style=social)  | KDD 2024\n| Foundation \u003cbr\u003e Traffic | TaxiBJ \u003cbr\u003e Crawd \u003cbr\u003e BikeNYC \u003cbr\u003e Cellular \u003cbr\u003e TDrive \u003cbr\u003e TrafficSH |    UniST | [UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction](https://dl.acm.org/doi/abs/10.1145/3637528.3671662) |  [Pytorch](https://github.com/tsinghua-fib-lab/UniST)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/tsinghua-fib-lab/UniST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/tsinghua-fib-lab/UniST?color=critical\u0026style=social)  | KDD 2024\n| Early \u003cbr\u003e Traffic | METR-LA \u003cbr\u003e EMS \u003cbr\u003e NYPD  |    STEMO | [STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning](https://dl.acm.org/doi/abs/10.1145/3637528.3671922) |  [Pytorch](https://github.com/coco0106/MO-STEP)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/coco0106/MO-STEP?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/coco0106/MO-STEP?color=critical\u0026style=social)  | KDD 2024\n| New nodes \u003cbr\u003e Traffic | Large-ST |    STONE | [STONE: A Spatio-temporal OOD Learning Framework Kills Both Spatial and Temporal Shifts](https://dl.acm.org/doi/abs/10.1145/3637528.3671680) |  [Pytorch](https://github.com/PoorOtterBob/STONE-KDD-2024)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/PoorOtterBob/STONE-KDD-2024?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/PoorOtterBob/STONE-KDD-2024?color=critical\u0026style=social)  | KDD 2024\n| Irregular \u003cbr\u003e Traffic | Zhuzhou \u003cbr\u003e Baoding |    Aseer | [Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networks](https://dl.acm.org/doi/abs/10.1145/3637528.3671665) |  [Pytorch](https://github.com/usail-hkust/ASeer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/usail-hkust/ASeer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/usail-hkust/ASeer?color=critical\u0026style=social)  | KDD 2024\n| Multivariable | Stock \u003cbr\u003e Exchange \u003cbr\u003e Weather   |    CONTIME | [Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization](https://dl.acm.org/doi/abs/10.1145/3637528.3671969) |  [Pytorch](https://github.com/sheoyon-jhin/CONTIME)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/sheoyon-jhin/CONTIME?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/sheoyon-jhin/CONTIME?color=critical\u0026style=social)  | KDD 2024\n| Multivariable | PEMS07 \u003cbr\u003e Large-ST  |    GWT | [Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks](https://dl.acm.org/doi/abs/10.1145/3637528.3671912) |  [Pytorch](https://anonymous.4open.science/r/paper-1430)  | KDD 2024\n| Large Scale   | Large-ST  |    RPMixer | [RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data](https://dl.acm.org/doi/abs/10.1145/3637528.3671881) |  [Pytorch](https://sites.google.com/view/rpmixer)   | KDD 2024\n| Demand Supply \u003cbr\u003e Prediction | Shanghai \u003cbr\u003e Zhengzhou   |    MulSTE | [MulSTE: A Multi-view Spatio-temporal Learning Framework with Heterogeneous Event Fusion for Demand-supply Prediction](https://dl.acm.org/doi/abs/10.1145/3637528.3672030) |  [Pytorch](https://github.com/mulste-kdd2024/MulSTE)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/mulste-kdd2024/MulSTE?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/mulste-kdd2024/MulSTE?color=critical\u0026style=social)  | KDD 2024\n| Multivariable | 108s   |    AutoXPCR | [AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3637528.3672057) |  [TF](https://github.com/raphischer/xpcr)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/raphischer/xpcr?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/raphischer/xpcr?color=critical\u0026style=social)  | KDD 2024\n| Multivariable |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |    UniTime | [UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3589334.3645434) | [Pytorch](https://github.com/liuxu77/UniTime)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/liuxu77/UniTime?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/liuxu77/UniTime?color=critical\u0026style=social)  | WWW 2024\n| Multivariable | Ross \u003cbr\u003e Saratoga \u003cbr\u003e  UpperPen  \u003cbr\u003e SFC  |    DAN | [Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/27768) | [Pytorch](https://github.com/davidanastasiu/dan)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/davidanastasiu/dan?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/davidanastasiu/dan?color=critical\u0026style=social)  | AAAI 2024\n| Multivariable | ILI \u003cbr\u003e Weather \u003cbr\u003e  Traffic  \u003cbr\u003e Electricity \u003cbr\u003e  ETT \u003cbr\u003e Exchange  |    HDMixer | [HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/29155) | [Pytorch](https://github.com/hqh0728/HDMixer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/hqh0728/HDMixer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/hqh0728/HDMixer?color=critical\u0026style=social)  | AAAI 2024\n| Multivariable |  PEMS03 \u003cbr\u003e PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08  \u003cbr\u003e  England \u003cbr\u003e  TaxiBJ \u003cbr\u003e  PEMS-BAY  |  STPGNN  | [Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/28707) | None  | AAAI 2024\n| Multivariable | FD001 \u003cbr\u003e FD002 \u003cbr\u003e  FD003  \u003cbr\u003e FD004  |    FC-STGNN | [Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data](https://ojs.aaai.org/index.php/AAAI/article/view/29500) | [Pytorch](https://github.com/Frank-Wang-oss/FCSTGNN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Frank-Wang-oss/FCSTGNN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Frank-Wang-oss/FCSTGNN?color=critical\u0026style=social)  | AAAI 2024\n| Multivariable | PEMS04  \u003cbr\u003e PEMS08 \u003cbr\u003e blockchain  |   TMP-Nets  | [Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence](https://ojs.aaai.org/index.php/AAAI/article/view/29051) | None  | AAAI 2024\n| Multivariable | METR-LA \u003cbr\u003e PEMS-BAY   |  ModWaveMLP | [ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/28753) | [TF](https://github.com/Kqingzheng/ModWaveMLP)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Kqingzheng/ModWaveMLP?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Kqingzheng/ModWaveMLP?color=critical\u0026style=social)  | AAAI 2024\n| Multivariable |Flight    \u003cbr\u003e Weather  \u003cbr\u003e ETT \u003cbr\u003e  Electricity  \u003cbr\u003e Exchange   |  MSGNet | [MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/28991) | [Pytorch](https://github.com/YoZhibo/MSGNet)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/YoZhibo/MSGNet?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/YoZhibo/MSGNet?color=critical\u0026style=social)  | AAAI 2024\n| Multivariable |  Self-PeMS  |  DLF | [Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective](https://ojs.aaai.org/index.php/AAAI/article/view/28759) | [Pytorch](https://github.com/wangbinwu13116175205/DLF)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/wangbinwu13116175205/DLF?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/wangbinwu13116175205/DLF?color=critical\u0026style=social)  | AAAI 2024\n| Multivariable |ETT    \u003cbr\u003e Weather  \u003cbr\u003e ILI  \u003cbr\u003e Exchange   |   HTV-Trans | [Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/29483) | [Pytorch](https://github.com/flare200020/HTV_Trans)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/flare200020/HTV_Trans?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/flare200020/HTV_Trans?color=critical\u0026style=social)  | AAAI 2024\n| Multivariable |A-share   \u003cbr\u003e Cross-Market  \u003cbr\u003e ETT   |  ST-DAN| [Adaptive Meta-Learning Probabilistic Inference Framework for Long Sequence Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/29661) | [Pytorch](https://github.com/Zhu-JP/AMPIF)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Zhu-JP/AMPIF?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Zhu-JP/AMPIF?color=critical\u0026style=social)  | AAAI 2024\n| Six  |  [TimesNet_data](https://github.com/thuml/Time-Series-Library)   |  CTRL | [An NCDE-based Framework for Universal Representation Learning of Time Series](https://www.ijcai.org/proceedings/2024/511) | [Pytorch](https://github.com/LiuZH-19/CTRL)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/LiuZH-19/CTRL?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/LiuZH-19/CTRL?color=critical\u0026style=social)  | IJCAI 2024\n| Traffic  | PEMS3478   |  STD-MAE | [Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting](https://www.ijcai.org/proceedings/2024/442) | [Pytorch](https://github.com/Jimmy-7664/STD-MAE)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Jimmy-7664/STD-MAE?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Jimmy-7664/STD-MAE?color=critical\u0026style=social)  | IJCAI 2024\n| Multivariable   |    [TimesNet_data](https://github.com/thuml/Time-Series-Library)   | DERITS | [Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting](https://www.ijcai.org/proceedings/2024/436) | None   | IJCAI 2024\n| Multivariable   |    [TimesNet_data](https://github.com/thuml/Time-Series-Library)   | Skip-Timeformer | [Skip-Timeformer: Skip-Time Interaction Transformer for Long Sequence Time-Series Forecasting](https://www.ijcai.org/proceedings/2024/608) | None  | IJCAI 2024\n| Multivariable   |    [TimesNet_data](https://github.com/thuml/Time-Series-Library)   | VCformer | [VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting](https://www.ijcai.org/proceedings/2024/590) | [Pytorch](https://github.com/CSyyn/VCformer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/CSyyn/VCformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/CSyyn/VCformer?color=critical\u0026style=social)  | IJCAI 2024\n| Multivariable   |    [TimesNet_data](https://github.com/thuml/Time-Series-Library)   | LeRet | [LeRet: Language-Empowered Retentive Network for Time Series Forecasting](https://www.ijcai.org/proceedings/2024/460) | [Pytorch](https://github.com/hqh0728/LeRet)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/hqh0728/LeRet?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/hqh0728/LeRet?color=critical\u0026style=social)  | IJCAI 2024\n| Missing Variate   |    METR-LA \u003cbr\u003e Solar \u003cbr\u003e Traffic \u003cbr\u003e ECG5000  | SDformer | [SDformer: Transformer with Spectral Filter and Dynamic Attention for Multivariate Time Series Long-term Forecasting](https://www.ijcai.org/proceedings/2024/228) | None  | IJCAI 2024\n| Traffic   |    METR-LA \u003cbr\u003e PEMS-BAY \u003cbr\u003e PEMSD7M  | DCST | [Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction](https://www.ijcai.org/proceedings/2024/288) | [Pytorch](https://github.com/ibizatomorrow/DCST)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/ibizatomorrow/DCST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/ibizatomorrow/DCST?color=critical\u0026style=social)  | IJCAI 2024\n| Traffic   |    METR-LA \u003cbr\u003e PEMS-BAY   | ST-nFBST | [Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting](https://www.ijcai.org/proceedings/2024/245) | [Pytorch](https://github.com/liuzh-buaa/ST-nFBST)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/liuzh-buaa/ST-nFBST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/liuzh-buaa/ST-nFBST?color=critical\u0026style=social)  | IJCAI 2024\n| multi-source  \u003cbr\u003e  SSL |  BikeIn \u003cbr\u003e BikeOut \u003cbr\u003e TaxiIn \u003cbr\u003e TaxiOut \u003cbr\u003e Air  | MoSSL | [Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning](https://www.ijcai.org/proceedings/2024/223) | [Pytorch](https://github.com/beginner-sketch/MoSSL)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/beginner-sketch/MoSSL?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/beginner-sketch/MoSSL?color=critical\u0026style=social)  | IJCAI 2024\n| Traffic \u003cbr\u003e CrossCity  |    METR-LA \u003cbr\u003e PEMS-BAY \u003cbr\u003e DiDiCD \u003cbr\u003e DiDiSZ  | pFedCTP  | [Personalized Federated Learning for Cross-City Traffic Prediction](https://www.ijcai.org/proceedings/2024/611) | [Pytorch](https://github.com/ZYuSdu/pFedCTP)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/ZYuSdu/pFedCTP?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/ZYuSdu/pFedCTP?color=critical\u0026style=social)  | IJCAI 2024\n| Multivariable   |    [TimesNet_data](https://github.com/thuml/Time-Series-Library)   | SDformer | [SDformer: Transformer with Spectral Filter and Dynamic Attention for Multivariate Time Series Long-term Forecasting](https://www.ijcai.org/proceedings/2024/629) | [Pytorch](https://github.com/zhouziyu02/SDformer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/zhouziyu02/SDformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/zhouziyu02/SDformer?color=critical\u0026style=social)  | IJCAI 2024\n| Multivariable   |    [TimesNet_data](https://github.com/thuml/Time-Series-Library)   | SpecAR-Net | [SpecAR-Net: Spectrogram Analysis and Representation Network for Time Series](https://www.ijcai.org/proceedings/2024/433) | [Pytorch](https://github.com/Dongyi2go/SpecAR_Net)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Dongyi2go/SpecAR_Net?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Dongyi2go/SpecAR_Net?color=critical\u0026style=social)  | IJCAI 2024\n| Multivariable   |    [TimesNet_data](https://github.com/thuml/Time-Series-Library)   | SCAT | [SCAT: A Time Series Forecasting with Spectral Central Alternating Transformers](https://www.ijcai.org/proceedings/2024/622) | None | IJCAI 2024\n| Traffic  | Traffic \u003cbr\u003e ECG \u003cbr\u003e  COVID-19 \u003cbr\u003e Wiki \u003cbr\u003e Solar   | DIAN | [Decoupled Invariant Attention Network for Multivariate Time-series Forecasting](https://www.ijcai.org/proceedings/2024/275) | [Pytorch](https://github.com/xhh39/DIAN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/xhh39/DIAN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/xhh39/DIAN?color=critical\u0026style=social)  | IJCAI 2024\n| Traffic  | Wave \u003cbr\u003e Wind \u003cbr\u003e  Air   | EPL | [Self-adaptive Extreme Penalized Loss for Imbalanced Time Series Prediction](https://www.ijcai.org/proceedings/2024/568) | [Pytorch](https://github.com/Ldiper/EPL)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Ldiper/EPL?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Ldiper/EPL?color=critical\u0026style=social)  | IJCAI 2024\n| Multivariable | ETT \u003cbr\u003e Electricity \u003cbr\u003e  Traffic  \u003cbr\u003e Weather   \u003cbr\u003e Exchange  |    U-Mixer | [U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/29337) | None | AAAI 2024\n| Irregular  |USHCN    \u003cbr\u003e MIMIC-III  \u003cbr\u003e MIMIC-IV  \u003cbr\u003e Physionet-12   |  GraFITi | [GraFITi: Graphs for Forecasting Irregularly Sampled Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/29560) | [Pytorch](https://github.com/yalavarthivk/GraFITi)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/yalavarthivk/GraFITi?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/yalavarthivk/GraFITi?color=critical\u0026style=social)  | AAAI 2024\n| Traffic \u003cbr\u003e Flow |  PEMS03 \u003cbr\u003e PEMS04 \u003cbr\u003e PEMS07 \u003cbr\u003e PEMS08  | MultiSPANS  | [MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization](https://dl.acm.org/doi/10.1145/3616855.3635820) |  [Pytorch](https://github.com/SELGroup/MultiSPANS)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/SELGroup/MultiSPANS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/SELGroup/MultiSPANS?color=critical\u0026style=social)   | WSDM 2024\n| Multivariable | SIP  \u003cbr\u003e NYC   \u003cbr\u003e METR-LA  | CreST  | [CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting](https://dl.acm.org/doi/10.1145/3616855.3635759) | None  | WSDM 2024\n| Multivariable | Web Traffic  \u003cbr\u003e Labour   \u003cbr\u003e Traffic \u003cbr\u003e  Tourism   | HTS  | [NeuralReconciler for Hierarchical Time Series Forecasting](https://dl.acm.org/doi/10.1145/3616855.3635806) | None  | WSDM 2024\n| Multivariable | NYC13    \u003cbr\u003e BikeNYC   \u003cbr\u003e Chicago21  \u003cbr\u003e  Chicago22   | CityCAN  | [CityCAN: Causal Attention Network for Citywide Spatio-Temporal Forecasting](https://dl.acm.org/doi/10.1145/3616855.3635764) | None  | WSDM 2024\n| Multivariable |  Solar \u003cbr\u003e Wiki \u003cbr\u003e  Traffic \u003cbr\u003e ECG \u003cbr\u003e Electricity  \u003cbr\u003e  COVID-19   \u003cbr\u003e Weather  \u003cbr\u003e  ETT |    FreTS | [Frequency-domain MLPs are More Effective Learners in Time Series Forecasting](https://proceedings.neurips.cc/paper_files/paper/2023/hash/f1d16af76939f476b5f040fd1398c0a3-Abstract-Conference.html) | [Pytorch](https://github.com/aikunyi/FreTS)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/aikunyi/FreTS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/aikunyi/FreTS?color=critical\u0026style=social)  | NIPS 2023\n| LLM4TS \u003cbr\u003e Zero Shot |  Darts  \u003cbr\u003e Monash  \u003cbr\u003e  Informer   |    - | [Large Language Models Are Zero-Shot Time Series Forecasters](https://proceedings.neurips.cc/paper_files/paper/2023/hash/3eb7ca52e8207697361b2c0fb3926511-Abstract-Conference.html) | [LLM](https://github.com/ngruver/llmtime)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/ngruver/llmtime?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/ngruver/llmtime?color=critical\u0026style=social)  | NIPS 2023\n| Zero Shot |  ECL  \u003cbr\u003eETT  \u003cbr\u003e Exchange \u003cbr\u003e ILI \u003cbr\u003e Traffic   \u003cbr\u003e   Weather    |    ForecastPFN | [ForecastPFN: Synthetically-Trained Zero-Shot Forecasting](https://proceedings.neurips.cc/paper_files/paper/2023/hash/0731f0e65559059eb9cd9d6f44ce2dd8-Abstract-Conference.html) | [TF](https://github.com/abacusai/forecastpfn)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/abacusai/forecastpfn?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/abacusai/forecastpfn?color=critical\u0026style=social)  | NIPS 2023\n| Multivariable |  ECL   \u003cbr\u003e  Traffic  \u003cbr\u003eETT \u003cbr\u003e   Weather    |    WITRAN | [WITRAN: Water-wave Information Transmission and Recurrent Acceleration Network for Long-range Time Series Forecasting](https://proceedings.neurips.cc/paper_files/paper/2023/hash/2938ad0434a6506b125d8adaff084a4a-Abstract-Conference.html) | [Pytorch](https://github.com/Water2sea/WITRAN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Water2sea/WITRAN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Water2sea/WITRAN?color=critical\u0026style=social)  | NIPS 2023\n| Multivariable |  ETT  \u003cbr\u003e  Weather   \u003cbr\u003e  PEMS03 \u003cbr\u003e PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08    |    Neural Lad | [Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling](https://openreview.net/forum?id=bISkJSa5Td) | None | NIPS 2023\n| Multivariable |  ETT  \u003cbr\u003e Weather  \u003cbr\u003e  Electricity   |    OneNet | [OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling](https://proceedings.neurips.cc/paper_files/paper/2023/hash/dd6a47bc0aad6f34aa5e77706d90cdc4-Abstract-Conference.html) | [Pytorch](https://github.com/yfzhang114/OneNet)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/yfzhang114/OneNet?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/yfzhang114/OneNet?color=critical\u0026style=social)  | NIPS 2023\n| Multivariable \u003cbr\u003e Solar Irradiance|  CAB \u003cbr\u003e TAM  |    CrossViVit | [Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context](https://openreview.net/forum?id=x5ZruOa4ax) | [Pytorch](https://github.com/gitbooo/CrossViVit)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/gitbooo/CrossViVit?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/gitbooo/CrossViVit?color=critical\u0026style=social)  | NIPS 2023\n| Multivariable |  ECL  \u003cbr\u003eETT \u003cbr\u003e Exchange \u003cbr\u003e  ILI \u003cbr\u003e  Traffic  \u003cbr\u003e  Weather    |    Koopa | [Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors](https://proceedings.neurips.cc/paper_files/paper/2023/hash/dd6a47bc0aad6f34aa5e77706d90cdc4-Abstract-Conference.html) | [Pytorch](https://github.com/thuml/Koopa)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/thuml/Koopa?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/thuml/Koopa?color=critical\u0026style=social)  | NIPS 2023\n| Multivariable | GPVAR \u003cbr\u003e METR-LA \u003cbr\u003e PEMS-BAY \u003cbr\u003e PEMS03 \u003cbr\u003e PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08  \u003cbr\u003e CER-E\u003cbr\u003eAQI     |    TTS-IMP | [Taming Local Effects in Graph-based Spatiotemporal Forecasting](https://openreview.net/forum?id=x2PH6q32LR) | [Pytorch](https://github.com/Graph-Machine-Learning-Group/taming-local-effects-stgnns)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Graph-Machine-Learning-Group/taming-local-effects-stgnns?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Graph-Machine-Learning-Group/taming-local-effects-stgnns?color=critical\u0026style=social)  | NIPS 2023\n| Multivariable |  PEMS08 \u003cbr\u003e AIR-BJ \u003cbr\u003e  AIR-GZ     |    CaST | [Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment](https://openreview.net/forum?id=17Zkztjlgt) | [Pytorch](https://github.com/yutong-xia/CaST)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/yutong-xia/CaST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/yutong-xia/CaST?color=critical\u0026style=social)  | NIPS 2023\n| Multivariable |  PEMS08 \u003cbr\u003e METR-LA \u003cbr\u003e  NYC Taxi \u003cbr\u003e NYC Bike     |    GPT-ST | [GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks](https://openreview.net/forum?id=nMH5cUaSj8) | [Pytorch](https://github.com/HKUDS/GPT-ST)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/HKUDS/GPT-ST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/HKUDS/GPT-ST?color=critical\u0026style=social)  | NIPS 2023\n| Multivariable |  Solar  \u003cbr\u003e Wiki \u003cbr\u003e Traffic \u003cbr\u003e COVID-19     |    FourierGNN | [FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective](https://proceedings.neurips.cc/paper_files/paper/2023/hash/dc1e32dd3eb381dbc71482f6a96cbf86-Abstract-Conference.html) | [Pytorch](https://github.com/aikunyi/FourierGNN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/aikunyi/FourierGNN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/aikunyi/FourierGNN?color=critical\u0026style=social)  | NIPS 2023\n| Multivariable | ETT  \u003cbr\u003e   Weather  \u003cbr\u003e Electricity  \u003cbr\u003e Traffic    |    SimMTM | [SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling](https://proceedings.neurips.cc/paper_files/paper/2023/hash/5f9bfdfe3685e4ccdbc0e7fb29cccf2a-Abstract-Conference.html) | [Pytorch](https://github.com/thuml/SimMTM)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/thuml/SimMTM?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/thuml/SimMTM?color=critical\u0026style=social)  | NIPS 2023\n| Multivariable | ETT \u003cbr\u003e Electricity  \u003cbr\u003e  Exchange  \u003cbr\u003e  Traffic  \u003cbr\u003e  Weather   \u003cbr\u003e  ILI   |    BasisFormer | [BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis](https://proceedings.neurips.cc/paper_files/paper/2023/hash/e150e6d0a1e5214740c39c6e4503ba7a-Abstract-Conference.html) | [Pytorch](https://github.com/nzl5116190/Basisformer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/nzl5116190/Basisformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/nzl5116190/Basisformer?color=critical\u0026style=social)  | NIPS 2023\n| Irregular |  Neonate  \u003cbr\u003e Traffic  \u003cbr\u003e  MIMIC \u003cbr\u003e  StackOverflow \u003cbr\u003e BookOrder \u003cbr\u003e Exchange \u003cbr\u003e ETT \u003cbr\u003e ILI\u003cbr\u003e  Weather|    ContiFormer | [ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling](https://proceedings.neurips.cc/paper_files/paper/2023/hash/9328208f88ec69420031647e6ff97727-Abstract-Conference.html) | [Pytorch](https://github.com/microsoft/SeqML/tree/main/ContiFormer)   | NIPS 2023\n| Multivariable |  Electricity \u003cbr\u003e Exchange \u003cbr\u003e Traffic \u003cbr\u003e  Weather  \u003cbr\u003e  ILI  \u003cbr\u003e ETT  |    SAN | [Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective](https://proceedings.neurips.cc/paper_files/paper/2023/hash/2e19dab94882bc95ed094c4399cfda02-Abstract-Conference.html) | [Pytorch](https://github.com/icantnamemyself/SAN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/icantnamemyself/SAN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/icantnamemyself/SAN?color=critical\u0026style=social)  | NIPS 2023\n| Multivariable |  ETT  \u003cbr\u003e Electricity \u003cbr\u003e Exchange \u003cbr\u003e Traffic  \u003cbr\u003e  Weather  \u003cbr\u003e  ILI   |    DeepTime (Framework, \u003cbr\u003e Fourier Features, \u003cbr\u003e Meta-optimization)| [ Learning Deep Time-index Models for Time Series Forecasting](https://openreview.net/forum?id=pgcfCCNQXO) | [Pytorch](https://github.com/salesforce/DeepTime)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/salesforce/DeepTime?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/salesforce/DeepTime?color=critical\u0026style=social)  | ICML 2023\n| Multivariable |  Crime \u003cbr\u003e CHI-Taxi  \u003cbr\u003e NYC-Bike \u003cbr\u003e NYC-Taxi\u003cbr\u003e CHI-House\u003cbr\u003e NYC-House    |  GraphST | [Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation](https://openreview.net/forum?id=LVARH5wXM9) | [Pytorch](https://github.com/HKUDS/GraphST)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/HKUDS/GraphST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/HKUDS/GraphST?color=critical\u0026style=social)  | ICML 2023\n| Multivariable |  Synthetic \u003cbr\u003e Taxi  \u003cbr\u003e Electricity \u003cbr\u003e Traffic    |    FeatureP (Feature Enhancement) | [Feature Programming for Multivariate Time Series Prediction](https://openreview.net/forum?id=LVARH5wXM9) | [Pytorch](https://github.com/SirAlex900/FeatureProgramming)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/SirAlex900/FeatureProgramming?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/SirAlex900/FeatureProgramming?color=critical\u0026style=social)  | ICML 2023\n| Multivariable |  NorPool  \u003cbr\u003e Caiso  \u003cbr\u003e Weather  \u003cbr\u003e  ETT   \u003cbr\u003e Wind \u003cbr\u003e Traffic  \u003cbr\u003e Electricity  \u003cbr\u003e Exchange |    TimeDiff    | [Non-autoregressive Conditional Diffusion Models for Time Series Prediction](https://openreview.net/forum?id=wZsnZkviro) | None| ICML 2023\n| Multivariable | ETT \u003cbr\u003e Electricity \u003cbr\u003e Exchange \u003cbr\u003e Traffic  \u003cbr\u003e Weather  \u003cbr\u003e  ILI  |    MICN    | [MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting](https://openreview.net/forum?id=zt53IDUR1U) | [Pytorch](https://github.com/wanghq21/MICN)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/wanghq21/MICN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/wanghq21/MICN?color=critical\u0026style=social) | ICLR 2023\n| Multivariable | ETT \u003cbr\u003e Weather \u003cbr\u003e Electricity \u003cbr\u003e  ILI  \u003cbr\u003e Traffic    |    Crossformer    | [Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting](https://openreview.net/forum?id=vSVLM2j9eie) | [Pytorch](https://github.com/Thinklab-SJTU/Crossformer)  \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Thinklab-SJTU/Crossformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Thinklab-SJTU/Crossformer?color=critical\u0026style=social)  | ICLR 2023\n| Forecast \u003cbr\u003e Imputation \u003cbr\u003e Classifi  \u003cbr\u003e AnomalyDet | ETT \u003cbr\u003e M4 \u003cbr\u003e Electricity \u003cbr\u003e  Weather  \u003cbr\u003eSMD,MSL \u003cbr\u003e SMAP,SWaT \u003cbr\u003e PSM  |    TimesNet    | [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://openreview.net/forum?id=ju_Uqw384Oq) | [Pytorch](https://github.com/thuml/Time-Series-Library) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/thuml/Time-Series-Library?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/thuml/Time-Series-Library?color=critical\u0026style=social) | ICLR 2023\n| Multivariable |  |    Meta-SSM    | [Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting](https://openreview.net/forum?id=7C9aRX2nBf2) | [Pytorch](https://github.com/john-x-jiang/meta_ssm) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/john-x-jiang/meta_ssm?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/john-x-jiang/meta_ssm?color=critical\u0026style=social) | ICLR 2023\n| Multivariable |  ETT \u003cbr\u003e Electricity  \u003cbr\u003e Traffic  \u003cbr\u003e Weather  |   FSNet   | [Learning Fast and Slow for Time Series Forecasting](https://openreview.net/forum?id=q-PbpHD3EOk) | [Pytorch](https://github.com/salesforce/fsnet) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/salesforce/fsnet?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/salesforce/fsnet?color=critical\u0026style=social) | ICLR 2023\n| Robust \u003cbr\u003e Multivariable |  Traffic \u003cbr\u003e Taxi  \u003cbr\u003e Wiki  \u003cbr\u003e Electricity  |        | [Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms](https://openreview.net/forum?id=ctmLBs8lITa) | [Amazon](https://github.com/awslabs/gluonts/tree/dev/src/gluonts/nursery) | ICLR 2023\n| Multivariable |  Electricity \u003cbr\u003e Crypto  \u003cbr\u003e M4  \u003cbr\u003e Traffic \u003cbr\u003e Exchange |   KNF     | [Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts](https://openreview.net/forum?id=kUmdmHxK5N) | [Pytorch](https://github.com/google-research/google-research/tree/master/KNF) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/google-research/google-research/tree/master/KNF?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/google-research/google-research/tree/master/KNF?color=critical\u0026style=social) | ICLR 2023\n| Multivariable |  ETT \u003cbr\u003e Weather  \u003cbr\u003e Electricity  \u003cbr\u003e Traffic \u003cbr\u003e Exchange |   SpaceTime     | [Effectively Modeling Time Series with Simple Discrete State Spaces](https://openreview.net/forum?id=2EpjkjzdCAa) | [Pytorch](https://github.com/HazyResearch/spacetime) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/HazyResearch/spacetime?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/HazyResearch/spacetime?color=critical\u0026style=social) | ICLR 2023\n| Multivariable |  Weather \u003cbr\u003e Traffic  \u003cbr\u003e Electricity  \u003cbr\u003e ILI \u003cbr\u003e ETT |   PatchTST     | [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://openreview.net/forum?id=Jbdc0vTOcol) | [Pytorch](https://github.com/yuqinie98/PatchTST) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/yuqinie98/PatchTST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/yuqinie98/PatchTST?color=critical\u0026style=social) | ICLR 2023\n| Multivariable |  Exchange  \u003cbr\u003e  Weather \u003cbr\u003e   Electricity \u003cbr\u003e Traffic  \u003cbr\u003e ILI  |   Scaleformer     | [Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting](https://openreview.net/forum?id=sCrnllCtjoE) | [Pytorch](https://github.com/BorealisAI/scaleformer) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/BorealisAI/scaleformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/BorealisAI/scaleformer?color=critical\u0026style=social) | ICLR 2023\n| Multivariable \u003cbr\u003e classification \u003cbr\u003e AnomalyDec |  Electricity  \u003cbr\u003e Weather \u003cbr\u003e ETTm1 \u003cbr\u003e MSL \u003cbr\u003e  SMD \u003cbr\u003e  SMAP   |    SBT    | [Sparse Binary Transformers for Multivariate Time Series Modeling](https://dl.acm.org/doi/abs/10.1145/3580305.3599508) |   [Pytorch](https://github.com/mattgorb/sparse-binary-transformers) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/mattgorb/sparse-binary-transformers?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/mattgorb/sparse-binary-transformers?color=critical\u0026style=social)   | KDD 2023\n| Multivariable |  SIP  \u003cbr\u003e METR-LA \u003cbr\u003e KnowAir \u003cbr\u003e Electricity |    CauSTG    | [Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning](https://dl.acm.org/doi/10.1145/3580305.3599529) |  [Pytorch](https://github.com/zzyy0929/KDD23-CauSTG) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/zzyy0929/KDD23-CauSTG?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/zzyy0929/KDD23-CauSTG?color=critical\u0026style=social)   | KDD 2023\n| Robust \u003cbr\u003e Multivariable |  PEMS-BAY  \u003cbr\u003e  PEMS04  |    RDAT    | [Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training](https://dl.acm.org/doi/10.1145/3580305.3599492) |  [Pytorch](https://github.com/usail-hkust/RDAT) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/usail-hkust/RDAT?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/usail-hkust/RDAT?color=critical\u0026style=social)   | KDD 2023\n| Multivariable | Beijing \u003cbr\u003e  Chengdu  \u003cbr\u003e Harbin  |    Frigate    | [Frigate: Frugal Spatio-temporal Forecasting on Road Networks](https://dl.acm.org/doi/10.1145/3580305.3599357) |  [Pytorch](https://github.com/idea-iitd/frigate) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/idea-iitd/frigate?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/idea-iitd/frigate?color=critical\u0026style=social)   | KDD 2023\n|Multivariable | XC-Traffic  \u003cbr\u003e  NYC-Traffic  |    GCIM    | [Generative Causal Interpretation Model for Spatio-Temporal Representation Learning](https://doi.org/10.1145/3580305.3599363) | None | KDD 2023\n| Multivariable |  Tourism  \u003cbr\u003e Labour \u003cbr\u003e Wiki \u003cbr\u003e Flu-Symptoms \u003cbr\u003e FB-Survey |    PROFHiT    | [When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting](https://dl.acm.org/doi/10.1145/3580305.3599529) |  [Pytorch](https://github.com/AdityaLab/Profhit) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/AdityaLab/Profhit?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/AdityaLab/Profhit?color=critical\u0026style=social)   | KDD 2023\n| Multivariable \u003cbr\u003e Under Miss |  AQI-36  \u003cbr\u003e AQI \u003cbr\u003e PEMS-BAY \u003cbr\u003e CER-E \u003cbr\u003e  Healthcare \u003cbr\u003e  SMAP   |    MIDM    | [An Observed Value Consistent Diffusion Model for Imputing Missing Values in Multivariate Time Series](https://doi.org/10.1145/3580305.3599257) |    [Author](http://home.ustc.edu.cn/~wx309/)   | KDD 2023\n| Multivariable |   PEMS03 \u003cbr\u003e PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08  \u003cbr\u003e etc.|    Localised    | [Localised Adaptive Spatial-Temporal Graph Neural Network](https://dl.acm.org/doi/10.1145/3580305.3599418) | None  | KDD 2023\n| Multivariable |  PEMS3-Stream   |    PECPM    | [Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction](https://doi.org/10.1145/3580305.3599463) | None  | KDD 2023\n| Multivariable |  Tourism  \u003cbr\u003e Wiki \u003cbr\u003e Traffic |    HPO    | [Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting](https://dl.acm.org/doi/10.1145/3580305.3599529) |   None | KDD 2023   \n| Multivariable |  Weather  \u003cbr\u003e Traffic  \u003cbr\u003e Electricity \u003cbr\u003e  ETT   |    TSMixer    | [TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting](https://doi.org/10.1145/3580305.3599533) | None| KDD 2023\n| Transfer \u003cbr\u003e Traffic \u003cbr\u003e Forecasting |  PEMSD7M  \u003cbr\u003e PEMSD7M \u003cbr\u003e METR-LA \u003cbr\u003e PEMS-BAY  |    TransGTR    | [Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities](https://dl.acm.org/doi/10.1145/3580305.3599529) |   [Author](https://github.com/KL4805)    | KDD 2023\n| Multivariable | ETT \u003cbr\u003e Traffic  \u003cbr\u003e Electricity   \u003cbr\u003e Exchange   \u003cbr\u003e Weather  \u003cbr\u003e   ILI  |  DLinear     | [Are Transformers Effective for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/26317) | [Pytorch](https://github.com/cure-lab/LTSF-Linear) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/cure-lab/LTSF-Linear?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/cure-lab/LTSF-Linear?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable | METR-LA  \u003cbr\u003e PEMSD7M  |  STC-Dropout    | [Easy Begun Is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout](https://ojs.aaai.org/index.php/AAAI/article/view/25590) | [Pytorch](https://github.com/Urban-Computing/STC-Dropout) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Urban-Computing/STC-Dropout?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Urban-Computing/STC-Dropout?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable | BJ-Bike \u003cbr\u003e NYC-Bike  |  STNSCM    | [Spatio-Temporal Neural Structural Causal Models for Bike Flow Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/25542) | [Pytorch](https://github.com/EternityZY/STNSCM) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/EternityZY/STNSCM?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/EternityZY/STNSCM?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable | XC-Trans \u003cbr\u003e XC-Speed  | CCHMM   | [Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/25619) | [Pytorch](https://github.com/EternityZY/CCHMM) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/EternityZY/CCHMM?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/EternityZY/CCHMM?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable | NYCBike1 \u003cbr\u003e NYCBike2 \u003cbr\u003e  NYCTaxi \u003cbr\u003e  BJTaxi |  ST-SSL    | [Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/25555) | [Pytorch](https://github.com/Echo-Ji/ST-SSL) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Echo-Ji/ST-SSL?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Echo-Ji/ST-SSL?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable | PV-US  \u003cbr\u003e CER-En  |  SGP     | [Scalable Spatiotemporal Graph Neural Networks](https://ojs.aaai.org/index.php/AAAI/article/view/25880) | [Pytorch](https://github.com/Graph-Machine-Learning-Group/sgp) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Graph-Machine-Learning-Group/sgp?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Graph-Machine-Learning-Group/sgp?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable | Electricity \u003cbr\u003e Solar  \u003cbr\u003e  PEMS-BAY  \u003cbr\u003e METR-LA |  SRD     | [Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling](https://ojs.aaai.org/index.php/AAAI/article/view/25915) | [Pytorch](https://github.com/Arthur-Null/SRD) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Arthur-Null/SRD?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Arthur-Null/SRD?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable | ETT  \u003cbr\u003e Electricity  |  InfoTS     | [Time Series Contrastive Learning with Information-Aware Augmentations](https://ojs.aaai.org/index.php/AAAI/article/view/25575) | [Pytorch](https://github.com/chengw07/InfoTS) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/chengw07/InfoTS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/chengw07/InfoTS?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable |   PhysioNet  \u003cbr\u003e   MIMIC-III    \u003cbr\u003e  Activity  \u003cbr\u003e  Appliances Energy |  PrimeNet   | [PrimeNet: Pre-training for Irregular Multivariate Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/25876) | [Pytorch](https://github.com/ranakroychowdhury/PrimeNet) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/ranakroychowdhury/PrimeNet?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/ranakroychowdhury/PrimeNet?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable |   Electricity  \u003cbr\u003e  ETT    \u003cbr\u003e Weather  |   Dish-TS    | [Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/25914) | [Pytorch](https://github.com/weifantt/Dish-TS) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/weifantt/Dish-TS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/weifantt/Dish-TS?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable |  ETT \u003cbr\u003e Electricity   \u003cbr\u003e Exchange  \u003cbr\u003e Traffic    \u003cbr\u003e Weather  \u003cbr\u003e   ILI  |  NHITS   | [NHITS: Neural Hierarchical Interpolation for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/25854) | [Pytorch](https://github.com/Nixtla/neuralforecast) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Nixtla/neuralforecast?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Nixtla/neuralforecast?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable |   METR-LA  \u003cbr\u003e   ETT    \u003cbr\u003e Weather   |  MegaCRN   | [Spatio-Temporal Meta-Graph Learning for Traffic Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/25976) | [Pytorch](https://github.com/deepkashiwa20/MegaCRN) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/deepkashiwa20/MegaCRN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/deepkashiwa20/MegaCRN?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable | Santa \u003cbr\u003e Traffic  |   NEC+     | [An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks](https://ojs.aaai.org/index.php/AAAI/article/view/26276) | [Pytorch](https://github.com/davidanastasiu/NECPlus) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/davidanastasiu/NECPlus?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/davidanastasiu/NECPlus?color=critical\u0026style=social) | AAAI 2023  \n| Extreme MTSF | Electricity  \u003cbr\u003e Solar  \u003cbr\u003e Weather \u003cbr\u003e Traffic  |   WaveForM     | [WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/26276) | [Pytorch](https://github.com/alanyoungCN/WaveForM) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/alanyoungCN/WaveForM?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/alanyoungCN/WaveForM?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable | PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08    \u003cbr\u003e  NYCTaxi   \u003cbr\u003e  CHBike  \u003cbr\u003e  TDrive |   PDFormer     | [PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/25556) | [Pytorch](https://github.com/BUAABIGSCity/PDFormer) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/BUAABIGSCity/PDFormer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/BUAABIGSCity/PDFormer?color=critical\u0026style=social) | AAAI 2023  \n| Multivariable |  AmapBeijing \u003cbr\u003e AmapChengdu   |   STGNPP     | [Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/26669) | None | AAAI 2023\n| Multivariable |  ETT \u003cbr\u003e Electricity  \u003cbr\u003e Exchange   \u003cbr\u003e Traffic \u003cbr\u003e Weather \u003cbr\u003e  ILI |   InParformer     | [InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/25845) | None | AAAI 2023\n| Multivariable |   Tourism  \u003cbr\u003e  Labour   \u003cbr\u003e   M5   |  SLOTH   | [SLOTH: Structured Learning and Task-Based Optimization for Time Series Forecasting on Hierarchies](https://ojs.aaai.org/index.php/AAAI/article/view/26350) | None | AAAI 2023  \n| Multivariable |   Wind \u003cbr\u003e  Solar    |  eForecaster   | [eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms](https://ojs.aaai.org/index.php/AAAI/article/view/26853) | None | AAAI 2023  \n| Multivariable | NYCTaxi \u003cbr\u003e PEMS04 |  AutoSTL    | [AutoSTL: Automated Spatio-Temporal Multi-Task Learning](https://ojs.aaai.org/index.php/AAAI/article/view/25616) | None | AAAI 2023  \n| Multivariable | METR-LA \u003cbr\u003e PEMS-BAY |    Trafformer    | [Trafformer: Unify Time and Space in Traffic Prediction](https://doi.org/10.1609/aaai.v37i7.25980) | None| AAAI 2023\n| Multivariable | Electricity \u003cbr\u003e  PM2.5  \u003cbr\u003e Exchange   |   DeLELSTM     | [DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series](https://www.ijcai.org/proceedings/2023/478) | [Pytorch](https://github.com/wangcq01/DeLELSTM) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/wangcq01/DeLELSTM?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/wangcq01/DeLELSTM?color=critical\u0026style=social) | IJCAI 2023  \n| Multivariable | NYC-Bike \u003cbr\u003e PEMS-BAY   \u003cbr\u003e  PEMS08 |   ReMo     | [Not Only Pairwise Relationships: Fine-Grained Relational Modeling for Multivariate Time Series Forecasting](https://www.ijcai.org/proceedings/2023/491) | [Pytorch](https://github.com/beginner-sketch/gmrl) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/beginner-sketch/gmrl?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/beginner-sketch/gmrl?color=critical\u0026style=social) | IJCAI 2023  \n| Multivariable | NASA |   MetePFL     | [Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data](https://www.ijcai.org/proceedings/2023/393) | [Pytorch](https://github.com/shengchaochen82/MetePFL) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/shengchaochen82/MetePFL?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/shengchaochen82/MetePFL?color=critical\u0026style=social) | IJCAI 2023  \n| Multivariable |  Hurricane \u003cbr\u003e  Climate   |   Self-Recover     | [Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning](https://www.ijcai.org/proceedings/2023/4141) | None  | IJCAI 2023  \n| Multivariable | Weather \u003cbr\u003e  Traffc  \u003cbr\u003e Electricity    \u003cbr\u003e  Exchange   \u003cbr\u003e  ILI   |   SMARTformer     | [SMARTformer: Semi-Autoregressive Transformer with Efficient Integrated Window Attention for Long Time Series Forecasting](https://www.ijcai.org/proceedings/2023/241) | None| IJCAI 2023  \n| Multivariable | METR-LA \u003cbr\u003e Beijing \u003cbr\u003e Xiamen |    INCREASE    | [INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging](https://dl.acm.org/doi/abs/10.1145/3543507.3583525) | [TF](https://github.com/zhengchuanpan/INCREASE) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/zhengchuanpan/INCREASE?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/zhengchuanpan/INCREASE?color=critical\u0026style=social)  | WWW 2023\n| Multivariable | MQPS \u003cbr\u003e ETT \u003cbr\u003e Electricity |    KAE-Informer    | [KAE-Informer: A Knowledge Auto-Embedding Informer for Forecasting Long-Term Workloads of Microservices](https://doi.org/10.1145/3543507.3583288) | [Pytorch](https://github.com/citsjtu2020/KAE-Informer-MQPS) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/citsjtu2020/KAE-Informer-MQPS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/citsjtu2020/KAE-Informer-MQPS?color=critical\u0026style=social)  | WWW 2023\n| Multivariable | Typhoon-JP \u003cbr\u003e COVID-JP \u003cbr\u003e Hurricane-US |    MemeSTN    | [Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster](https://doi.org/10.1145/3543507.3583991) | [Pytorch](https://github.com/citsjtu2020/KAE-Informer-MQPS) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/citsjtu2020/KAE-Informer-MQPS?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/citsjtu2020/KAE-Informer-MQPS?color=critical\u0026style=social)  | WWW 2023\n| Multivariable |   NYC  \u003cbr\u003e  Chicago  |  EALGAP   | [Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning](https://ieeexplore.ieee.org/document/10184645) | [Keras](https://github.com/HuiqunHuang/EALGAP) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/HuiqunHuang/EALGAP?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/HuiqunHuang/EALGAP?color=critical\u0026style=social) | ICDE 2023  \n| Multivariable | PEMS03 \u003cbr\u003e PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08  |  DyHSL   | [Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting](https://ieeexplore.ieee.org/document/10184800) | [Pytorch](https://github.com/YushengZhao/DyHSL) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/YushengZhao/DyHSL?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/YushengZhao/DyHSL?color=critical\u0026style=social) | ICDE 2023  \n| Multivariable | PEMS03 \u003cbr\u003e PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08  |  STWave   | [When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks](https://ieeexplore.ieee.org/document/10184591) | [Pytorch](https://github.com/LMissher/STWave) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/LMissher/STWave?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/LMissher/STWave?color=critical\u0026style=social) | ICDE 2023  \n| Multivariable | Seattle \u003cbr\u003e PEMS04  \u003cbr\u003e PEMS08  |  SSTBAN   | [Self-Supervised Spatial-Temporal Bottleneck Attentive Network for Efficient Long-term Traffic Forecasting](https://ieeexplore.ieee.org/document/10184658) | [Pytorch](https://github.com/guoshnBJTU/SSTBAN) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/guoshnBJTU/SSTBAN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/guoshnBJTU/SSTBAN?color=critical\u0026style=social) | ICDE 2023  \n| Multivariable | PEMSD4 \u003cbr\u003e PEMSD8 \u003cbr\u003e AirBJ \u003cbr\u003e TrafficSIP |   MGTF   | [A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework](https://dl.acm.org/doi/10.1145/3539597.3570396) | [Author](http://home.ustc.edu.cn/~wx309/)  | WSDM 2023\n| Multivariable |  METR-LA \u003cbr\u003e PEMS-BAY  \u003cbr\u003e PEMS04 \u003cbr\u003e PEMS07 \u003cbr\u003e PEMS08|  STAEformer   | [Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting](https://dl.acm.org/doi/10.1145/3583780.3615136) | [Pytorch](https://github.com/XDZhelheim/STAEformer) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/XDZhelheim/STAEformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/XDZhelheim/STAEformer?color=critical\u0026style=social) | CIKM 2023  \n| Traffic | PEMS03 \u003cbr\u003e PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08 |  TrendGCN   | [Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting](https://dl.acm.org/doi/10.1145/3583780.3614868) | [Pytorch](https://github.com/juyongjiang/TrendGCN) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/juyongjiang/TrendGCN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/juyongjiang/TrendGCN?color=critical\u0026style=social) | CIKM 2023  \n| Multivariable | ETT \u003cbr\u003e Electricity  \u003cbr\u003e Traffic   \u003cbr\u003e Weather \u003cbr\u003e ILI \u003cbr\u003e  Exchange |  GCformer   | [GCformer: An Efficient Solution for Accurate and Scalable Long-Term Multivariate Time Series Forecasting](https://dl.acm.org/doi/10.1145/3583780.3614868) | [Pytorch](https://github.com/Yanjun-Zhao/GCformer) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Yanjun-Zhao/GCformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Yanjun-Zhao/GCformer?color=critical\u0026style=social) | CIKM 2023  \n| Multivariable | ETT \u003cbr\u003e Electricity  \u003cbr\u003e Traffic |  Seq2Peak  | [Unlocking the Potential of Deep Learning in Peak-Hour Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3583780.3615159) | [Pytorch](https://github.com/zhangzw16/Seq2Peak) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/zhangzw16/Seq2Peak?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/zhangzw16/Seq2Peak?color=critical\u0026style=social) | CIKM 2023   \n| Multivariable |  PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08  \u003cbr\u003e NYC Crime  \u003cbr\u003e CHI Crime |  CL4ST  | [Spatio-Temporal Meta Contrastive Learning](https://dl.acm.org/doi/10.1145/3583780.3615065) | [Pytorch](https://github.com/HKUDS/CL4ST) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/HKUDS/CL4ST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/HKUDS/CL4ST?color=critical\u0026style=social) | CIKM 2023  \n| Multivariable | NYC Bike  \u003cbr\u003e NYC Taxi    |  MLPST | [MLPST: MLP is All You Need for Spatio-Temporal Prediction](https://dl.acm.org/doi/10.1145/3583780.3614969) | [Author](https://github.com/Zhang-Zijian)  | CIKM 2023  \n| Multivariable | TaxiBJ  \u003cbr\u003e BikeNYC    |  MC-STL  | [Mask- and Contrast-Enhanced Spatio-Temporal Learning for Urban Flow Prediction](https://dl.acm.org/doi/10.1145/3583780.3614958) | [Pytorch](https://github.com/CodeZx6/MCSTL) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/CodeZx6/MCSTL?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/CodeZx6/MCSTL?color=critical\u0026style=social) | CIKM 2023  \n| Multivariable | PeMS  \u003cbr\u003e Beijing  \u003cbr\u003e Electricity   \u003cbr\u003e COVID-CHI |  MemDA   | [MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation](https://dl.acm.org/doi/10.1145/3583780.3615136) | [Pytorch](https://github.com/deepkashiwa20/Urban_Concept_Drift) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/deepkashiwa20/Urban_Concept_Drift?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/deepkashiwa20/Urban_Concept_Drift?color=critical\u0026style=social) | CIKM 2023  \n| Cross City \u003cbr\u003e Traffic |   PEMS-BAY  \u003cbr\u003e METR-LA   \u003cbr\u003e Chengdu   \u003cbr\u003e   Shenzhen|  TPB  | [Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank](https://dl.acm.org/doi/10.1145/3583780.3614829) | [Pytorch](https://github.com/zhyliu00/TPB) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/zhyliu00/TPB?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/zhyliu00/TPB?color=critical\u0026style=social) | CIKM 2023  \n| Traffic Speed | METR-LA \u003cbr\u003e PEMS-BAY  \u003cbr\u003e PEMSD7M  |  UAGCRN  | [Enhancing Spatio-temporal Traffic Prediction through Urban Human Activity Analysis](https://dl.acm.org/doi/10.1145/3583780.3614867) | [TF](https://github.com/SuminHan/Traffic-UAGCRNTF) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/SuminHan/Traffic-UAGCRNTF?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/SuminHan/Traffic-UAGCRNTF?color=critical\u0026style=social) | CIKM 2023  \n| Multivariable | Complaint \u003cbr\u003e NYC Taxi    |  PromptST  | [PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction](https://dl.acm.org/doi/abs/10.1145/3583780.3615159) | [Pytorch](https://github.com/Zhang-Zijian/PromptST) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/Zhang-Zijian/PromptST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/Zhang-Zijian/PromptST?color=critical\u0026style=social) | CIKM 2023  \n| Multivariable | METR-LA \u003cbr\u003e PEMS-BAY  \u003cbr\u003e PEMS08 |  HIEST  | [Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting](https://dl.acm.org/doi/10.1145/3583780.3614910) | [Pytorch](https://github.com/VAN-QIAN/CIKM23-HIEST) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/VAN-QIAN/CIKM23-HIEST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/VAN-QIAN/CIKM23-HIEST?color=critical\u0026style=social) | CIKM 2023  \n| Multivariable | ETT \u003cbr\u003e Electricity  \u003cbr\u003e Weather  \u003cbr\u003e Traffic |  TemDep  | [TemDep: Temporal Dependency Priority for Multivariate Time Series Prediction](https://dl.acm.org/doi/10.1145/3583780.3615164) | [Pytorch](https://github.com/zivgogogo/TemDep) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/zivgogogo/TemDep?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/zivgogogo/TemDep?color=critical\u0026style=social) | CIKM 2023  \n| Traffic |  BJ-Center  \u003cbr\u003e  METR-LA |  ST-MoE  | [ST-MoE: Spatio-Temporal Mixture-of-Experts for Debiasing in Traffic Prediction](https://dl.acm.org/doi/10.1145/3583780.3615068) | None  | CIKM 2023  \n| Multivariable | ETT \u003cbr\u003e Electricity  \u003cbr\u003e Weather  \u003cbr\u003e Traffic  \u003cbr\u003e  Exchange  |  AVGNets  | [Learning Visibility Attention Graph Representation for Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3583780.3615289) | None | CIKM 2023  \n| Multivariable |  PEMS03  \u003cbr\u003e PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08   |  STGBN  | [Spatial-Temporal Graph Boosting Networks: Enhancing Spatial-Temporal Graph Neural Networks via Gradient Boosting](https://dl.acm.org/doi/10.1145/3583780.3615066) | None  | CIKM 2023 \n| Multivariable | ETT \u003cbr\u003e Electricity  \u003cbr\u003e Traffic   \u003cbr\u003e ILI \u003cbr\u003e  Exchange | FAMC-Net   | [FAMC-Net: Frequency Domain Parity Correction Attention and Multi-Scale Dilated Convolution for Time Series Forecasting](https://dl.acm.org/doi/10.1145/3583780.3614876) | None| CIKM 2023  \n| Cross City \u003cbr\u003e Traffic |  NYC  \u003cbr\u003e Chicago    \u003cbr\u003e Nashville   |  CARPG  | [CARPG: Cross-City Knowledge Transfer for Traffic Accident Prediction via Attentive Region-Level Parameter Generation](https://dl.acm.org/doi/abs/10.1145/3583780.3614802) | None| CIKM 2023  \n| Traffic | SPEED \u003cbr\u003e FLOW  |  CANet  | [Clustering-property Matters: A Cluster-aware Network for Large Scale Multivariate Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3583780.3615253) | None  | CIKM 2023  \n| Multivariable | ETT \u003cbr\u003e Exchange  \u003cbr\u003e ILI   \u003cbr\u003e Weather  \u003cbr\u003e  Electricity  \u003cbr\u003e Traffic |  DSformer   | [DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction](https://dl.acm.org/doi/abs/10.1145/3583780.3614851) | None | CIKM 2023 \n| Multivariable | Wufu    |  MODE    | [Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data](https://dl.acm.org/doi/10.1145/3583780.3615487) |  None  | CIKM 2023  \n| Multivariable | NYC  |  MetaRSTP  | [Region Profile Enhanced Urban Spatio-Temporal Prediction via Adaptive Meta-Learning](https://dl.acm.org/doi/10.1145/3583780.3615027) |    None  | CIKM 2023  \n| Multivariable | SIP \u003cbr\u003e  NYC  \u003cbr\u003e METR-LA |    G2S    | [Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics](https://epubs.siam.org/doi/abs/10.1137/1.9781611977653.ch22) | None | SDM 2023\n| Multivariable | Solar \u003cbr\u003e  PEMS-BAY \u003cbr\u003e Electricity |    ERL    | [Time-delayed Multivariate Time Series Predictions](https://epubs.siam.org/doi/abs/10.1137/1.9781611977653.ch37) | None | SDM 2023\n| Multivariable | Weather2K   |   Weather2K     | [Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations](https://proceedings.mlr.press/v206/zhu23a.html) | [Weather2K](https://github.com/bycnfz/weather2k/) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/bycnfz/weather2k?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/bycnfz/weather2k?color=critical\u0026style=social) | AISTATS 2023  \n| Multivariable | ETT \u003cbr\u003e Electricity  \u003cbr\u003e Exchange   \u003cbr\u003e Traffic \u003cbr\u003e Weather \u003cbr\u003e  ILI |    FiLM    | [FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting](https://openreview.net/forum?id=zTQdHSQUQWc) | [Pytorch](https://github.com/tianzhou2011/FiLM) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/tianzhou2011/FiLM?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/tianzhou2011/FiLM?color=critical\u0026style=social) | NeurIPS 2022\n| Multivariable | ETT \u003cbr\u003e Electricity  \u003cbr\u003e Exchange  \u003cbr\u003e Weather |    LaST    | [Learning Latent Seasonal-Trend Representations for Time Series Forecasting](https://openreview.net/forum?id=C9yUwd72yy) | [Pytorch](https://github.com/zhycs/LaST) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/zhycs/LaST?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/zhycs/LaST?color=critical\u0026style=social) | NeurIPS 2022\n| Multivariable | ETT \u003cbr\u003e Electricity  \u003cbr\u003e Exchange  \u003cbr\u003e Traffic \u003cbr\u003e Weather \u003cbr\u003e  ILI |    WaveBound    | [WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting](https://openreview.net/forum?id=vsNQkquutZk) | [Pytorch](https://github.com/choyi0521/WaveBound) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/choyi0521/WaveBound?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/choyi0521/WaveBound?color=critical\u0026style=social) | NeurIPS 2022\n| Multivariable | COVID-19 \u003cbr\u003e PEMS04  \u003cbr\u003e PEMS08  \u003cbr\u003e Temperature \u003cbr\u003e Bytom \u003cbr\u003e  Wind |    ZFC-SHCN    | [Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting](https://openreview.net/forum?id=2Ln-TWxVtf) | [Future](https://github.com/zfcshcn/ZFC-SHCN) | NeurIPS 2022\n| Multivariable | ETT \u003cbr\u003e Traffic  \u003cbr\u003e Solar  \u003cbr\u003e Electricity \u003cbr\u003e Exchange  \u003cbr\u003e    PEMS03 \u003cbr\u003e PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08  |    SCINet    | [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://openreview.net/forum?id=AyajSjTAzmg) | [Pytorch](https://github.com/cure-lab/SCINet) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/cure-lab/SCINet?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/cure-lab/SCINet?color=critical\u0026style=social) | NeurIPS 2022\n| Multivariable | Electricity \u003cbr\u003e ETT  \u003cbr\u003e Exchange  \u003cbr\u003e  ILI  \u003cbr\u003e Traffic \u003cbr\u003e Weather |   NonstaTransformer   | [Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting](https://openreview.net/forum?id=ucNDIDRNjjv) | [Pytorch](https://github.com/thuml/Nonstationary_Transformers) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/thuml/Nonstationary_Transformers?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/thuml/Nonstationary_Transformers?color=critical\u0026style=social) | NeurIPS 2022\n| Multivariable | Traffic \u003cbr\u003e Solar  \u003cbr\u003e Electricity  \u003cbr\u003e  Exchange  \u003cbr\u003e PEMS07(M) \u003cbr\u003e PEMS-BAY |   TPGNN   | [Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks](https://openreview.net/forum?id=pMumil2EJh) | [Future](https://github.com/zyplanet/TPGNN) | NeurIPS 2022\n| Multivariable | PEMS03 \u003cbr\u003e PEMS04 \u003cbr\u003e  PEMS07  \u003cbr\u003e PEMS08  |    DSTAGNN    | [DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting](https://proceedings.mlr.press/v162/lan22a.html) | [Pytorch](https://github.com/SYLan2019/DSTAGNN) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/SYLan2019/DSTAGNN?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/SYLan2019/DSTAGNN?color=critical\u0026style=social) | ICML 2022\n| Multivariable | ETT \u003cbr\u003e Electricity  \u003cbr\u003e Exchange  \u003cbr\u003e Traffic \u003cbr\u003e Weather \u003cbr\u003e ILI |        FEDformer \u003cbr\u003e (EncDec,\u003cbr\u003e EnhancedFeature)      | [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://proceedings.mlr.press/v162/zhou22g.html) | [Pytorch](https://github.com/MAZiqing/FEDformer) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/MAZiqing/FEDformer?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/MAZiqing/FEDformer?color=critical\u0026style=social) | ICML 2022\n| Multivariable | Traffic \u003cbr\u003e Electricity  \u003cbr\u003e Wiki  \u003cbr\u003e Sales  |       DAF     | [DAF-Domain Adaptation for Time Series Forecasting via Attention Sharing](https://proceedings.mlr.press/v162/jin22d.html) | None| ICML 2022\n| Multivariable | Electricity  \u003cbr\u003e Solar  \u003cbr\u003e Fred MD \u003cbr\u003e KDD Cup  |        TACTiS \u003cbr\u003e (Copulas,\u003cbr\u003e Trans)      | [TACTiS: Transformer-Attentional Copulas for Time Series](https://proceedings.mlr.press/v162/drouin22a.html) | [Pytorch](https://github.com/servicenow/tactis) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/servicenow/tactis?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://img.shields.io/github/forks/servicenow/tactis?color=critical\u0026style=social) | ICML 2022\n| Multivariable | French \u003cbr\u003e Electricity    |        AgACI      | [Adaptive Conformal Predictions for Time Series](https://arxiv.org/abs/2202.07282) | [Python,R](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries) \u003cbr\u003e![Stars](https://img.shields.io/github/stars/mzaffran/AdaptiveConformalPredictionsTimeSeries?color=critical\u0026style=social) \u003cbr\u003e![Forks](https://","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flixus7%2FTime-Series-Works-Conferences","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flixus7%2FTime-Series-Works-Conferences","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flixus7%2FTime-Series-Works-Conferences/lists"}