{"id":13472164,"url":"https://github.com/DaoSword/Time-Series-Forecasting-and-Deep-Learning","last_synced_at":"2025-03-26T15:31:31.065Z","repository":{"id":49394023,"uuid":"517530479","full_name":"DaoSword/Time-Series-Forecasting-and-Deep-Learning","owner":"DaoSword","description":"Resources about time series forecasting and deep learning.","archived":false,"fork":false,"pushed_at":"2024-10-29T17:35:20.000Z","size":773,"stargazers_count":561,"open_issues_count":2,"forks_count":54,"subscribers_count":26,"default_branch":"main","last_synced_at":"2024-10-29T18:57:25.654Z","etag":null,"topics":["data-science","deep-learning","forecasting","machine-learning","series-data","series-forecasting","time-series","time-series-forecasting"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DaoSword.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2022-07-25T05:32:22.000Z","updated_at":"2024-10-29T17:35:24.000Z","dependencies_parsed_at":"2023-12-25T10:37:51.986Z","dependency_job_id":"f589cd19-3528-471a-a097-4a447f0d2b66","html_url":"https://github.com/DaoSword/Time-Series-Forecasting-and-Deep-Learning","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/DaoSword%2FTime-Series-Forecasting-and-Deep-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DaoSword%2FTime-Series-Forecasting-and-Deep-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DaoSword%2FTime-Series-Forecasting-and-Deep-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DaoSword%2FTime-Series-Forecasting-and-Deep-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DaoSword","download_url":"https://codeload.github.com/DaoSword/Time-Series-Forecasting-and-Deep-Learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245681331,"owners_count":20655172,"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":["data-science","deep-learning","forecasting","machine-learning","series-data","series-forecasting","time-series","time-series-forecasting"],"created_at":"2024-07-31T16:00:52.510Z","updated_at":"2025-03-26T15:31:31.044Z","avatar_url":"https://github.com/DaoSword.png","language":null,"funding_links":[],"categories":["Others"],"sub_categories":[],"readme":"# Time Series Forecasting and Deep Learning\n\n![GitHub commit activity](https://img.shields.io/github/commit-activity/m/DaoSword/Time-Series-Forecasting-and-Deep-Learning?style=flat-square)\n![GitHub last commit](https://img.shields.io/github/last-commit/DaoSword/Time-Series-Forecasting-and-Deep-Learning?style=flat-square)\n![GitHub closed issues](https://img.shields.io/github/issues-closed/DaoSword/Time-Series-Forecasting-and-Deep-Learning?style=flat-square)\n![GitHub forks](https://img.shields.io/github/forks/DaoSword/Time-Series-Forecasting-and-Deep-Learning?style=flat-square)\n![GitHub Repo stars](https://img.shields.io/github/stars/DaoSword/Time-Series-Forecasting-and-Deep-Learning?style=flat-square)\n\nList of research papers focus on time series forecasting and deep learning, as well as other resources like competitions, datasets, courses, blogs, code, etc.\n\n## Table of Contents\n\n- [Applications](#Applications)\n- [Benchmarks](#Benchmarks)\n- [Papers](#Papers)\n  - [2025](#2025)\n  - [2024](#2024)\n  - [2023](#2023)\n  - [2022](#2022)\n  - [2021](#2021)\n  - [2020](#2020)\n  - [2019](#2019)\n  - [2018](#2018)\n  - [2017](#2017)\n- [Blogs](#Blogs)\n- [Competitions](#Competitions)\n- [Courses](#Courses)\n- [Libraries](#Libraries)\n- [Datasets](#Datasets)\n- [Books](#Books)\n- [Repositories](#Repositories)\n- [Tutorials](#Tutorials)\n\n## Applications\n\n- [TimeGPT](https://docs.nixtla.io/)\n\n  - Nixtla’s `TimeGPT` is a generative pre-trained forecasting model for time series data. \n\n## Benchmarks\n\n- [FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting](https://github.com/TongjiFinLab/FinTSB)\n\n  - `FinTSB` is a comprehensive and practical financial time series benchmark.\n\n- [GIFT-Eval Time Series Forecasting Leaderboard](https://huggingface.co/spaces/Salesforce/GIFT-Eval)\n\n  -  `GIFT-Eval` is a pioneering benchmark aimed at promoting evaluation across diverse datasets.\n\n## Papers\n\n### 2025\n\n- [TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting](https://arxiv.org/abs/2502.06910)\n\n  - 26 Feb 2025, Songtao Huang, et al.\n \n  - [[Official Code - TimeKAN](https://github.com/huangst21/TimeKAN)]\n\n- [TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation](https://arxiv.org/abs/2410.05711)\n\n  - 21 Feb 2025, Daoyu Wang, et al.\n \n  - [[Official Code - TimeDART](https://github.com/Melmaphother/TimeDART)]\n \n- [Harnessing Vision Models for Time Series Analysis: A Survey](https://arxiv.org/abs/2502.08869)\n\n  - 13 Feb 2025, Jingchao Ni, et al.\n\n  - [[Official Code - awesome-vision-time-series](https://github.com/D2I-Group/awesome-vision-time-series)]\n\n- [TVNet: A Novel Time Series Analysis Method Based on Dynamic Convolution and 3D-Variation](https://openreview.net/forum?id=MZDdTzN6Cy)\n\n  - 23 Jan 2025, Chenghan Li, et al.\n\n- [TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting](https://arxiv.org/abs/2501.13041)\n\n  - 22 Jan 2025, Yifan Hu, et al.\n \n  - [[Official Code - TimeFilter](https://github.com/troubadour000/timefilter)]\n\n- [Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation](https://arxiv.org/abs/2501.04970)\n\n  - 09 Jan 2025, HyunGi Kim, et al.\n \n  - [[Official Code - TAFAS](https://github.com/kimanki/TAFAS)]\n \n- [The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features](https://arxiv.org/abs/2501.02945)\n\n  - 09 Jan 2025, Shi Bin Hoo, et al.\n \n  - [[Official Code - tabpfn-time-series](https://github.com/liam-sbhoo/tabpfn-time-series)]\n \n- [Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series](https://arxiv.org/abs/2501.03747)\n\n  - 07 Jan 2025, Yuxiao Hu, et al.\n \n- [LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting](https://arxiv.org/abs/2412.06866)\n\n  - 07 Jan 2025, Ibrahim Delibasoglu, et al.\n \n  - [[Official Code - LMS-TSF](https://github.com/mribrahim/LMS-TSF)]\n\n### 2024\n\n- [AverageLinear: Enhance Long-Term Time series forcasting with simple averaging](https://arxiv.org/abs/2412.20727)\n\n  - 30 Dec 2024, Gaoxiang Zhao, et al.\n \n  - [[Official Code - AverageLinear](https://github.com/UniqueoneZ/AverageLinear)]\n\n- [TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting](https://arxiv.org/abs/2412.20810)\n\n  - 30 Dec 2024, Huanyu Zhang, et al.\n \n- [Unlocking the Power of Patch: Patch-Based MLP for Long-Term Time Series Forecasting](https://arxiv.org/abs/2405.13575)\n\n  - 25 Dec 2024, Peiwang Tang, et al.\n \n- [Hierarchical Classification Auxiliary Network for Time Series Forecasting](https://arxiv.org/abs/2405.18975)\n\n  - 24 Dec 2024, Yanru Sun, et al.\n \n  - [[Official Code - HCAN](https://github.com/syrgithub/hcan)]\n\n- [DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting](https://arxiv.org/abs/2412.10859)\n\n  - 23 Dec 2024, Xiangfei Qiu, et al.\n \n  - [[Official Code - DUET](https://github.com/decisionintelligence/duet)]\n \n- [WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting](https://arxiv.org/abs/2412.17176)\n\n  - 22 Dec 2024, Md Mahmuddun Nabi Murad, et al.\n \n  - [[Official Code - WPMixer](https://github.com/Secure-and-Intelligent-Systems-Lab/WPMixer)]\n \n- [TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation](https://arxiv.org/abs/2412.16643)\n\n  - 21 Dec 2024, Silin Yang, et al.\n\n- [Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine](https://arxiv.org/abs/2412.14435)\n\n  - 19 Dec 2024, Luis Roque, et al.\n \n  - [[Official Code - bench](https://github.com/luisroque/bench)]\n  \n- [LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting](https://arxiv.org/abs/2410.17159)\n\n  - 17 Dec 2024, Guoqi Yu, et al.\n \n  - [[Official Code - LiNo](https://github.com/levi-ackman/lino)]\n \n- [ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data](https://arxiv.org/abs/2412.11376)\n\n  - 16 Dec 2024, Chengsen Wang, et al.\n \n  - [[Official Code - ChatTime](https://github.com/forestsking/chattime)]\n \n- [ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis](https://arxiv.org/abs/2403.01493)\n\n  - 14 Dec 2024, Mingyue Cheng, et al.\n \n  - [[Official Code - ConvTimeNet](https://github.com/Mingyue-Cheng/ConvTimeNet)]\n \n- [Auto-Regressive Moving Diffusion Models for Time Series Forecasting](https://arxiv.org/abs/2412.09328)\n\n  - 12 Dec 2024, Jiaxin Gao, et al.\n \n  - [[Official Code - ARMD](https://github.com/daxin007/ARMD)]\n\n- [Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning](https://arxiv.org/abs/2412.04806)\n\n  - 06 Dec 2024, Jayanie Bogahawatte, et al.\n\n- [Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting](https://arxiv.org/abs/2411.17257)\n\n  - 26 Nov 2024, Yuang Zhao, et al.\n \n  - [[Official Code - DiPE-Linear](https://github.com/wintertee/dipe-linear)]\n\n- [Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective](https://arxiv.org/abs/2409.18696)\n\n  - 13 Nov 2024, Chengsen Wang, et al.\n \n  - [[Official Code - GLAFF](https://github.com/ForestsKing/GLAFF)]\n \n- [Scaling Law for Time Series Forecasting](https://arxiv.org/abs/2405.15124)\n\n  - 09 Nov 2024, Jingzhe Shi, et al.\n \n  - [[Official Code - ScalingLawForTimeSeriesForecasting](https://github.com/jingzheshi/scalinglawfortimeseriesforecasting)]\n \n- [EffiCANet: Efficient Time Series Forecasting with Convolutional Attention](https://arxiv.org/abs/2411.04669)\n\n  - 07 Nov 2024, Xinxing Zhou, et al.\n \n- [Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis](https://arxiv.org/abs/2411.04554)\n\n  - 07 Nov 2024, Qiang Wu, et al.\n \n  - [[Official Code - Peri-midFormer](https://github.com/WuQiangXDU/Peri-midFormer)]\n\n- [From Similarity to Superiority: Channel Clustering for Time Series Forecasting](https://arxiv.org/abs/2404.01340)\n\n  - 06 Nov 2024, Jialin Chen, et al.\n \n  - [[Official Code - TimeSeriesCCM](https://github.com/graph-and-geometric-learning/timeseriesccm)]\n \n- [A Mamba Foundation Model for Time Series Forecasting](https://arxiv.org/abs/2411.02941)\n\n  - 05 Nov 2024, Haoyu Ma, et al.\n \n- [Cross-Domain Pre-training with Language Models for Transferable Time Series Representations](https://arxiv.org/abs/2403.12372)\n\n  - 05 Nov 2024, Mingyue Cheng, et al.\n \n  - [[Official Code - CrossTimeNet](https://github.com/mingyue-cheng/crosstimenet)]\n\n- [Not All Frequencies Are Created Equal:Towards a Dynamic Fusion of Frequencies in Time-Series Forecasting](https://arxiv.org/abs/2407.12415)\n\n  - 05 Nov 2024, Xingyu Zhang, et al.\n \n  - [[Offcial Code - FreDF](https://github.com/Zh-XY22/FreDF)]\n\n- [ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer](https://arxiv.org/abs/2411.01842)\n\n  - 04 Nov 2024, Jiawen Zhang, et al.\n \n  - [[Official Code - ElasTST](https://github.com/microsoft/ProbTS/tree/elastst)]\n\n- [FilterNet: Harnessing Frequency Filters for Time Series Forecasting](https://arxiv.org/abs/2411.01623)\n\n  - 03 Nov 2024, Kun Yi, et al.\n \n  - [[Official Code - FilterNet](https://github.com/aikunyi/filternet)]\n \n- [Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting](https://arxiv.org/abs/2410.23992)\n\n  - 31 Oct 2024, Zongjiang Shang, et al.\n \n  - [[Official Code - Ada-MSHyper](https://github.com/shangzongjiang/Ada-MSHyper)]\n \n- [FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities](https://arxiv.org/abs/2410.23160)\n\n  - 30 Oct 2024, Jingge Xiao, et al.\n \n  - [[Official Code - FlexTSF](https://github.com/jingge326/flextsf)]\n\n- [From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection](https://arxiv.org/abs/2409.17515)\n\n  - 30 Oct 2024, Xinlei Wang, et al.\n \n  - [[Official Code - From_News_to_Forecast](https://github.com/ameliawong1996/From_News_to_Forecast)]\n \n- [LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series Forecasting](https://dl.acm.org/doi/10.1145/3627673.3679527)\n\n  - 21 Oct 2024, Hubert Truchan, et al.\n \n  - [[Official Code - LTBoost](https://github.com/hubtru/LTBoost)]\n\n- [TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis](https://arxiv.org/abs/2410.16032)\n\n  - 21 Oct 2024, Shiyu Wang, et al.\n \n  - [[Official Code - TimeMixer](https://github.com/kwuking/TimeMixer)]\n\n- [HiPPO-KAN: Efficient KAN Model for Time Series Analysis](https://arxiv.org/abs/2410.14939)\n\n  - 19 Oct 2024, SangJong Lee, et al.\n\n- [Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting](https://arxiv.org/abs/2401.11929)\n\n  - 16 Oct 2024, Jinliang Deng, et al.\n \n- [Towards Neural Scaling Laws for Time Series Foundation Models](https://arxiv.org/abs/2410.12360)\n\n  - 16 Oct 2024, Qingren Yao, et al.\n\n- [FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting](https://arxiv.org/abs/2410.11802)\n\n  - 15 Oct 2024, Zhe Li, et al.\n \n- [LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting](https://arxiv.org/abs/2410.11674)\n\n  - 15 Oct 2024, Md Kowsher, et al.\n \n  - [[Official Code - LLMMixer](https://github.com/Kowsher/LLMMixer)]\n \n- [Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts](https://arxiv.org/abs/2410.10469)\n\n  - 14 Oct 2024, Xu Liu, et al.\n \n  - [[Official Code - moirai-moe-1](https://github.com/SalesforceAIResearch/uni2ts/tree/main/project/moirai-moe-1)]\n \n- [Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift](https://arxiv.org/abs/2410.09836)\n\n  - 13 Oct 2024, Yanru Sun, et al.\n \n  - [[Official Code - TFPS](https://github.com/syrGitHub/TFPS)]\n \n- [Are Self-Attentions Effective for Time Series Forecasting?](https://arxiv.org/abs/2405.16877)\n\n  - 12 Oct 2024, Dongbin Kim, et al.\n \n  - [[Official Code - CATS](https://github.com/dongbeank/cats)]\n\n- [Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models](https://arxiv.org/abs/2410.09385)\n\n  - 12 Oct 2024, Sathya Kamesh Bhethanabhotla, et al.\n \n  - [[Official Code - Mamba4Cast](https://github.com/automl/mamba4cast)]\n\n- [TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting](https://arxiv.org/abs/2410.04442)\n\n  - 12 Oct 2024, Peiyuan Liu, et al.\n \n  - [[Official Code - TimeBridge](https://github.com/hank0626/timebridge)]\n \n- [Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting](https://arxiv.org/abs/2405.14252)\n\n  - 08 Oct 2024, Qingxiang Liu, et al.\n\n- [Timer-XL: Long-Context Transformers for Unified Time Series Forecasting](https://arxiv.org/abs/2410.04803)\n\n  - 07 Oct 2024, Yong Liu, et al.\n\n- [Autoregressive Moving-average Attention Mechanism for Time Series Forecasting](https://arxiv.org/abs/2410.03159)\n\n  - 04 Oct 2024, Jiecheng Lu, et al.\n \n  - [[Official Code - ARMA-Attention](https://github.com/ljc-fvnr/arma-attention)]\n \n- [MMFNet: Multi-Scale Frequency Masking Neural Network for Multivariate Time Series Forecasting](https://arxiv.org/abs/2410.02070)\n\n  - 02 Oct 2024, Aitian Ma, et al.\n \n- [NuwaTS: a Foundation Model Mending Every Incomplete Time Series](https://arxiv.org/abs/2405.15317)\n\n  - 02 Oct 2024, Jinguo Cheng, et al.\n \n  - [[Official Code - NuwaTS](https://github.com/chengyui/nuwats)]\n \n- [Frequency Adaptive Normalization For Non-stationary Time Series Forecasting](https://arxiv.org/abs/2409.20371)\n\n  - 30 Sep 2024, Weiwei Ye, et al.\n \n  - [[Official Code - FAN](https://github.com/wayne155/FAN)]\n\n- [Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts](https://arxiv.org/abs/2409.19718)\n\n  - 29 Sep 2024, Dalin Qin, et al.\n \n  - [[Official Code - EvoMSN](https://github.com/qindalin/evomsn)]\n\n- [CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns](https://arxiv.org/abs/2409.18479)\n\n  - 27 Sep 2024, Shengsheng Lin, et al.\n \n  - [[Official Code - CycleNet](https://github.com/ACAT-SCUT/CycleNet)]\n\n- [CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting](https://arxiv.org/abs/2406.05316)\n\n  - 26 Sep 2024, Chaolv Zeng, et al.\n \n  - [[Official Code - CMamba](https://github.com/zclzcl0223/CMamba)]\n\n- [PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting](https://arxiv.org/abs/2409.17703)\n\n  - 26 Sep 2024, Yuxin Jia, et al.\n \n  - [[Official Code - TPGN](https://github.com/Water2sea/TPGN)]\n\n- [Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecasting](https://arxiv.org/abs/2409.15662)\n\n  - 24 Sep 2024, Wenbo Yan, et al.\n\n- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://arxiv.org/abs/2409.16040)\n\n  - 24 Sep 2024, Xiaoming Shi, et al.\n \n  - [[Official Code - Time-MoE](https://github.com/time-moe/time-moe)]\n \n- [TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model](https://arxiv.org/abs/2409.02322)\n\n  - 03 Sep 2024, Defu Cao, et al.\n\n- [VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters](https://arxiv.org/abs/2408.17253)\n\n  - 30 Aug 2024, Mouxiang Chen, et al.\n \n  - [[Official Code - VisionTS](https://github.com/keytoyze/visionts)]\n \n- [Mamba or Transformer for Time Series Forecasting? Mixture of Universals (MoU) Is All You Need](https://www.arxiv.org/abs/2408.15997)\n\n  - 28 Aug 2024, Sijia Peng, et al.\n \n  - [[Official Code - mou](https://github.com/lunaaa95/mou)]\n \n- [PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting](https://arxiv.org/abs/2408.10483)\n\n  - 20 Aug 2024, Yongbo Yu, et al.\n \n  - [[Official Code - PRformer](https://github.com/usualheart/prformer)]\n \n- [Unlocking the Power of LSTM for Long Term Time Series Forecasting](https://arxiv.org/abs/2408.10006)\n\n  - 19 Aug 2024, Yaxuan Kong, et al.\n\n- [Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators](https://arxiv.org/abs/2401.17548)\n\n  - 13 Aug 2024, Lifan Zhao, et al.\n \n  - [[Official Code - LIFT](https://github.com/sjtu-dmtai/lift)]\n\n- [Bidirectional Generative Pre-training for Improving Time Series Representation Learning](https://arxiv.org/abs/2402.09558)\n\n  - 11 Aug 2024, Ziyang Song, et al.\n \n  - [[Official Code - BiTimelyGPT](https://github.com/li-lab-mcgill/bitimelygpt)]\n \n- [Scalable Transformer for High Dimensional Multivariate Time Series Forecasting](https://arxiv.org/abs/2408.04245)\n\n  - 08 Aug 2024, Xin Zhou, et al.\n \n  - [[Official Code - ScalableTransformer4HighDimensionMTSF](https://github.com/xinzzzhou/scalabletransformer4highdimensionmtsf)]\n \n- [RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms](https://www.arxiv.org/abs/2408.03399)\n\n  - 06 Aug 2024, Luis Roque, et al.\n \n  - [[Official Code - robustness_hierarchical_time_series_forecasting_algorithms](https://github.com/luisroque/robustness_hierarchical_time_series_forecasting_algorithms)]\n \n- [Fine-grained Attention in Hierarchical Transformers for Tabular Time-series](https://arxiv.org/abs/2406.15327)\n\n  - 02 Aug 2024, Raphael Azorin, et al.\n \n  - [[Official Code - fieldy](https://github.com/raphaaal/fieldy)]\n \n- [DAM: Towards A Foundation Model for Time Series Forecasting](https://arxiv.org/abs/2407.17880)\n\n  - 25 Jul 2024, Luke Darlow, et al.\n\n- [A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting](https://arxiv.org/abs/2407.15909)\n\n  - 22 Jul 2024, Pierre-Daniel Arsenault, et al.\n \n- [Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization](https://arxiv.org/abs/2305.14543)\n\n  - 18 Jul 2024, Yirui Liu, et al.\n \n  - [[Official Code - df2m](https://github.com/yiruiliu110/df2m)]\n\n- [Deep Time Series Models: A Comprehensive Survey and Benchmark](https://arxiv.org/abs/2407.13278)\n\n  - 18 Jul 2024, Yuxuan Wang, et al.\n \n  - [[Official Code - Time-Series-Library](https://github.com/thuml/Time-Series-Library)]\n \n- [Long Input Sequence Network for Long Time Series Forecasting](https://arxiv.org/abs/2407.15869)\n\n  - 18 Jul 2024, Chao Ma, et al.\n \n- [Large Pre-trained time series models for cross-domain Time series analysis tasks](https://arxiv.org/abs/2311.11413)\n\n  - 11 Jul 2024, Harshavardhan Kamarthi, et al.\n \n- [Loss Shaping Constraints for Long-Term Time Series Forecasting](https://arxiv.org/abs/2402.09373)\n\n  - 11 Jul 2024, Ignacio Hounie, et al.\n\n- [ViTime: A Visual Intelligence-Based Foundation Model for Time Series Forecasting](https://arxiv.org/abs/2407.07311)\n\n  - 10 Jul 2024, Luoxiao Yang, et al.\n \n  - [[Official Code - ViTime](https://github.com/IkeYang/ViTime)]\n\n- [S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting](https://arxiv.org/abs/2403.05798)\n\n  - 07 Jul 2024, Zijie Pan, et al.\n \n  - [[Official Code - S2IP-LLM](https://github.com/panzijie825/s2ip-llm)]\n \n- [Fredformer: Frequency Debiased Transformer for Time Series Forecasting](https://arxiv.org/abs/2406.09009)\n\n  - 03 Jul 2024, Xihao Piao, et al.\n \n  - [[Official Code - Fredformer](https://github.com/chenzrg/fredformer)]\n\n- [Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling](https://arxiv.org/abs/2402.12694)\n\n  - 01 Jul 2024, Guoqi Yu, et al.\n \n  - [[Official Code - Leddam](https://github.com/Levi-Ackman/Leddam)]\n \n- [Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization](https://arxiv.org/abs/2407.01622)\n\n  - 29 Jun 2024, SheoYon Jhin, et al.\n \n  - [[Official Code - CONTIME](https://github.com/sheoyon-jhin/contime)]\n\n- [Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting](https://arxiv.org/abs/2407.00502)\n\n  - 29 Jun 2024, Wei Fan, et al.\n \n- [SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series](https://arxiv.org/abs/2406.17890)\n\n  - 25 Jun 2024, Hugo Inzirillo, et al.\n \n  - [[Official Code - SigKAN](https://github.com/remigenet/SigKAN)]\n\n- [Are Language Models Actually Useful for Time Series Forecasting?](https://arxiv.org/abs/2406.16964)\n\n  - 22 Jun 2024, Mingtian Tan, et al.\n \n  - [[Official Code - TS_Models](https://github.com/bennytmt/ts_models)]\n \n- [DeciMamba: Exploring the Length Extrapolation Potential of Mamba](https://arxiv.org/abs/2406.14528)\n\n  - 20 Jun 2024, Assaf Ben-Kish, et al.\n \n  - [[Official Code - DeciMamba](https://github.com/assafbk/decimamba)]\n \n- [Understanding Different Design Choices in Training Large Time Series Models](https://arxiv.org/abs/2406.14045)\n\n  - 20 Jun 2024, Yu-Neng Chuang, et al.\n \n  - [[Official Code - ltsm](https://github.com/daochenzha/ltsm/)]\n\n- [Omni-Dimensional Frequency Learner for General Time Series Analysis](https://arxiv.org/abs/2407.10419)\n\n  - 19 Jul 2024, Xianing Chen, et al.\n \n- [Foundation Models for Time Series Analysis: A Tutorial and Survey](https://arxiv.org/abs/2403.14735)\n\n  - 18 Jun 2024, Yuxuan Liang, et al.\n \n- [Generative Pretrained Hierarchical Transformer for Time Series Forecasting](https://arxiv.org/abs/2402.16516)\n\n  - 18 Jun 2024, Zhiding Liu, et al.\n \n  - [[Official Code - GPHT](https://github.com/icantnamemyself/gpht)]\n \n- [ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons](https://arxiv.org/abs/2310.07446)\n\n  - 17 Jun 2024, Jiawen Zhang, et al.\n \n  - [[Official Code - ProbTS](https://github.com/microsoft/probts)]\n\n- [LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction](https://arxiv.org/abs/2406.10811)\n\n  - 16 Jun 2024, Meiyun Wang, et al.\n \n- [RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data](https://arxiv.org/abs/2402.10487)\n\n  - 12 Jun 2024, Chin-Chia Michael Yeh, et al.\n\n- [SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion](https://arxiv.org/abs/2404.14197)\n\n  - 12 Jun 2024, Lu Han, et al.\n \n  - [[Official Code - SOFTS](https://github.com/secilia-cxy/softs)]\n \n- [Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis](https://arxiv.org/abs/2406.08627)\n\n  - 12 Jun 2024, Haoxin Liu, et al.\n \n  - [[Official Code - Time-MMD](https://github.com/adityalab/time-mmd)]\n \n- [A Survey on Diffusion Models for Time Series and Spatio-Temporal Data](https://arxiv.org/abs/2404.18886)\n\n  - 11 Jun 2024, Yiyuan Yang, et al.\n \n  - [[Official Code - Awesome-TimeSeries-SpatioTemporal-Diffusion-Model](https://github.com/yyysjz1997/awesome-timeseries-spatiotemporal-diffusion-model)]\n \n- [Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift](https://arxiv.org/abs/2310.14838)\n\n  - 11 Jun 2024, Mouxiang Chen, et al.\n \n  - [[Official Code - Calibration-CDS](https://github.com/half111/calibration_cds)]\n\n- [When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting](https://arxiv.org/abs/2402.12767)\n\n  - 07 Jun 2024, Zijian Li, et al.\n \n- [Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting](https://arxiv.org/abs/2406.03751)\n\n  - 06 Jun 2024, Yifan Hu, et al.\n \n  - [[Official Code - AMD](https://github.com/troubadour000/amd)]\n \n- [Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability](https://arxiv.org/abs/2406.02496)\n\n  - 04 Jun 2024, Kunpeng Xu, et al.\n \n- [Timer: Generative Pre-trained Transformers Are Large Time Series Models](https://arxiv.org/abs/2402.02368)\n\n  - 04 Jun 2024, Yong Liu, et al.\n \n  - [[Official Code - Large-Time-Series-Model](https://github.com/thuml/Large-Time-Series-Model)]\n \n- [SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention](https://arxiv.org/abs/2402.10198)\n\n  - 03 Jun 2024, Romain Ilbert, et al.\n \n  - [[Official Code - samformer](https://github.com/romilbert/samformer)]\n \n- [SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters](https://arxiv.org/abs/2405.00946)\n\n  - 03 Jun 2024, Shengsheng Lin, et al.\n \n  - [[Official Code - SparseTSF](https://github.com/lss-1138/SparseTSF)]\n \n- [BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition](https://arxiv.org/abs/2308.14906)\n\n  - 30 May 2024, Shikai Fang, et al.\n \n  - [[Official Code - BayOTIDE](https://github.com/xuangu-fang/bayotide)]\n\n- [Efficient and Effective Time-Series Forecasting with Spiking Neural Networks](https://arxiv.org/abs/2402.01533)\n\n  - 29 May 2024, Changze Lv, et al.\n\n  - [[Official Code - SeqSNN](https://github.com/microsoft/seqsnn)]\n \n- [UNITS: A Unified Multi-Task Time Series Model](https://arxiv.org/abs/2403.00131)\n\n  - 29 May 2024, Shanghua Gao, et al.\n \n  - [[Official Code - UniTS](https://github.com/mims-harvard/UniTS)]\n \n- [ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks](https://arxiv.org/abs/2405.18036)\n\n  - 28 May 2024, Wanlin Cai, et al.\n\n- [MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting](https://arxiv.org/abs/2405.16440)\n\n  - 26 May 2024, Xiuding Cai, et al.\n\n  - [[Official Code - MambaTS-pytorch](https://github.com/XiudingCai/MambaTS-pytorch)]\n \n- [CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning](https://arxiv.org/abs/2403.07300)\n\n  - 23 May 2024, Peiyuan Liu, et al.\n \n  - [[Official Code - CALF](https://github.com/Hank0626/CALF)]\n\n- [TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting](https://arxiv.org/abs/2405.14616)\n\n  - 23 May 2024, Shiyu Wang, et al.\n \n  - [[Official Code - TimeMixer](https://github.com/kwuking/TimeMixer)]\n \n- [GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing](https://arxiv.org/abs/2405.11333)\n\n  - 18 May 2024, Chengqing Yu, et al.\n \n  - [[Official Code - GinAR](https://github.com/chengqingyu/ginar)]\n \n- [Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting](https://arxiv.org/abs/2404.15772)\n\n  - 17 May 2024, Aobo Liang, et al.\n\n- [DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting](https://arxiv.org/abs/2405.08440)\n\n  - 14 May 2024, Qinshuo Liu, et al.\n \n- [Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting](https://arxiv.org/abs/2405.05499)\n\n  - 14 May 2024, Feifei Li, et al\n\n- [Kolmogorov-Arnold Networks (KANs) for Time Series Analysis](https://arxiv.org/abs/2405.08790)\n\n  - 14 May 2024, Cristian J. Vaca-Rubio, et al.\n \n- [TKAN: Temporal Kolmogorov-Arnold Networks](https://arxiv.org/abs/2405.07344)\n\n  - 12 May 2024, Remi Genet, et al.\n \n  - [[Official Code - TKAN](https://github.com/remigenet/tkan)]\n \n- [DTMamba : Dual Twin Mamba for Time Series Forecasting](https://arxiv.org/abs/2405.07022)\n\n  - 11 May 2024, Zexue Wu, et al.\n\n- [Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting](https://arxiv.org/abs/2405.06419)\n\n  - 10 May 2024, Tianxiang Zhan, et al.\n \n  - [[Official Code - TEFN](https://github.com/ztxtech/Time-Evidence-Fusion-Network)]\n \n- [T-Rep: Representation Learning for Time Series using Time-Embeddings](https://arxiv.org/abs/2310.04486)\n\n  - 09 May 2024, Archibald Fraikin, et al.\n \n  - [[Official Code - T-Rep](https://github.com/let-it-care/t-rep)]\n \n- [A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model](https://arxiv.org/abs/2405.02358)\n\n  - 07 May 2024, Jiexia Ye, et al.\n \n  - [[Official Code - Awesome-TimeSeries-LLM-FM](https://github.com/start2020/awesome-timeseries-llm-fm)]\n\n- [TSLANet: Rethinking Transformers for Time Series Representation Learning](https://arxiv.org/abs/2404.08472)\n\n  - 06 May 2024, Emadeldeen Eldele, et al.\n \n  - [[Official Code - TSLANet](https://github.com/emadeldeen24/tslanet)]\n \n- [Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach](https://openreview.net/forum?id=UZlMXUGI6e)\n\n  - 02 May 2024, Weijia Zhang, et al.\n \n- [Integrating Mamba and Transformer for Long-Short Range Time Series Forecasting](https://arxiv.org/abs/2404.14757)\n\n  - 23 Apr 2024, Xiongxiao Xu, et al.\n \n  - [[Official Code - Mambaformer-in-Time-Series](https://github.com/XiongxiaoXu/Mambaformer-in-Time-Series)]\n \n- [Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values](https://openreview.net/forum?id=O9nZCwdGcG)\n\n  - 21 Apr 2024, Xiaodan Chen, et al.\n \n  - [[Official Code - BiTGraph](https://github.com/chenxiaodanhit/BiTGraph)]\n \n- [A decoder-only foundation model for time-series forecasting](https://arxiv.org/abs/2310.10688)\n\n  - 17 Apr 2024, Abhimanyu Das, et al.\n \n  - [[Official Code - timesfm](https://github.com/google-research/timesfm)]\n\n- [Towards Transparent Time Series Forecasting](https://openreview.net/forum?id=TYXtXLYHpR)\n\n  - 15 Apr 2024, Krzysztof Kacprzyk, et al.\n \n- [Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series](https://arxiv.org/abs/2401.03955)\n\n  - 09 Apr 2024, Vijay Ekambaram, et al.\n \n  - [[Official Code - granite-tsfm](https://github.com/ibm-granite/granite-tsfm)]\n\n- [ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting](https://arxiv.org/abs/2404.05192)\n\n  - 08 Apr 2024, Hengyu Ye, et al.\n \n  - [[Official Code - ATFNet](https://github.com/yhyhyhyhyhy/atfnet)]\n\n- [OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting](https://arxiv.org/abs/2304.01506)\n\n  - 04 Apr 2023, Xiao He, et al.\n \n  - [[Official Code - OneShotSTL](https://github.com/xiao-he/oneshotstl)]\n\n- [Is Mamba Effective for Time Series Forecasting?](https://arxiv.org/abs/2403.11144)\n  \n  - 02 Apr 2024, Zihan Wang, et al.\n   \n  - [[Official Code - S-D-Mamba](https://github.com/wzhwzhwzh0921/S-D-Mamba)]\n \n- [TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting](https://arxiv.org/abs/2310.04948)\n\n  - 02 Apr 2024, Defu Cao, et al.\n \n  - [[Official Code - TEMPO](https://github.com/dc-research/tempo)]\n \n- [MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection](https://arxiv.org/abs/2403.19888)\n\n  - 29 Mar 2024, Ali Behrouz, et al.\n\n- [TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods](https://arxiv.org/abs/2403.20150)\n\n  - 29 Mar 2024, Xiangfei Qiu, et al.\n\n  - [[Official Code - TFB](https://github.com/decisionintelligence/TFB)]\n \n- [An Analysis of Linear Time Series Forecasting Models](https://arxiv.org/abs/2403.14587)\n\n  - 25 Mar 2024, William Toner, et al.\n \n  - [[Official Code - linear-forecasting](https://github.com/sir-lab/linear-forecasting)]\n \n- [An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock Forecasting](https://arxiv.org/abs/2404.07969)\n\n  - 25 Mar 2024, Chufeng Li, et al.\n \n  - [[Official Code - ACEFormer](https://github.com/durandallee/aceformer)]\n \n- [HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/29155)\n\n  - 24 Mar 2024, Qihe Huang, et al.\n \n- [Latent Diffusion Transformer for Probabilistic Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/29085)\n\n  - 24 Mar 2024, Shibo Feng, et al.\n \n- [StockMixer: A Simple Yet Strong MLP-Based Architecture for Stock Price Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/28681)\n\n  - 24 Mar 2024, Jinyong Fan, et al.\n \n  - [[Official Code - StockMixer](https://github.com/SJTU-Quant/StockMixer)]\n \n- [ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis](https://openreview.net/forum?id=vpJMJerXHU)\n\n  - 22 Mar 2024, Donghao Luo, et al.\n \n  - [[Official Code - ModernTCN](https://github.com/luodhhh/ModernTCN)]\n \n- [SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series](https://arxiv.org/abs/2403.15360)\n\n  - 22 Mar 2024, Badri N. Patro, et al.\n \n  - [[Official Code - simba](https://github.com/badripatro/simba)]\n \n- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://arxiv.org/abs/2310.06625)\n\n  - 14 Mar 2024, Yong Liu, et al.\n \n  - [[Official Code - iTransformer](https://github.com/thuml/iTransformer)]\n \n- [Self-Supervised Learning for Time Series: Contrastive or Generative?](https://arxiv.org/abs/2403.09809)\n\n  - 14 Mar 2024, Ziyu Liu, et al.\n \n  - [[Official Code - SSL_Comparison](https://github.com/dl4mhealth/ssl_comparison)]\n\n- [TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting](https://arxiv.org/abs/2403.09898)\n\n  - 14 Mar 2024, Md Atik Ahamed, et al.\n \n  - [[Official Code - TimeMachine](https://github.com/atik-ahamed/timemachine)]\n\n- [TimeDRL: Disentangled Representation Learning for Multivariate Time-Series](https://arxiv.org/abs/2312.04142)\n\n  - 13 Mar 2024, Ching Chang, et al.\n \n  - [[Official Code - TimeDRL](https://github.com/blacksnail789521/timedrl)]\n \n- [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815)\n\n  - 12 Mar 2024, Abdul Fatir Ansari, et al.\n \n  - [[Official Code - chronos-forecasting](https://github.com/amazon-science/chronos-forecasting)]\n\n- [Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning](https://arxiv.org/abs/2402.04852)\n\n  - 10 Mar 2024, Yuxuan Bian, et al.\n \n  - [[Official Code - aLLM4TS](https://github.com/yxbian23/aLLM4TS)]\n \n- [MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process](https://arxiv.org/abs/2403.05751)\n\n  - 09 Mar 2024, Xinyao Fan, et al.\n \n  - [[Official Code - MG-TSD](https://github.com/hundredl/mg-tsd)]\n \n- [Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting](https://arxiv.org/abs/2403.05406)\n\n  - 08 Mar 2024, Muyao Wang, et al.\n \n  - [[Official Code - HTV_Trans](https://github.com/flare200020/HTV_Trans)]\n \n- [Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting](https://arxiv.org/abs/2402.05956)\n\n  - 07 Mar 2024, Peng Chen, et al.\n \n  - [[Official Code - pathformer](https://github.com/decisionintelligence/pathformer)]\n\n- [Periodicity Decoupling Framework for Long-term Series Forecasting](https://openreview.net/forum?id=dp27P5HBBt)\n\n  - 06 Mar 2024, Tao Dai, et al.\n \n  - [[Official Code - PDF](https://github.com/Hank0626/PDF)]\n \n- [InjectTST: A Transformer Method of Injecting Global Information into Independent Channels for Long Time Series Forecasting](https://arxiv.org/abs/2403.02814)\n\n  - 05 Mar 2024, Ce Chi, et al.\n\n- [CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables](https://arxiv.org/abs/2403.01673)\n\n  - 04 Mar 2024, Jiecheng Lu, et al.\n \n  - [[Official Code - CATS](https://github.com/LJC-FVNR/CATS)]\n \n- [Diffusion-TS: Interpretable Diffusion for General Time Series Generation](https://arxiv.org/abs/2403.01742)\n\n  - 04 Mar 2024, Xinyu Yuan, et al.\n \n  - [[Official Code - Diffusion-TS](https://github.com/y-debug-sys/diffusion-ts)]\n \n- [Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models](https://arxiv.org/abs/2402.03659)\n\n  - 29 Feb 2024, Kelvin Koa, et al.\n \n  - [[Official Code - SEP](https://github.com/koa-fin/sep)]\n\n- [TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables](https://arxiv.org/abs/2402.19072)\n\n  - 29 Feb 2024, Yuxuan Wang, et al.\n \n  - [[Official Code - TimeXer](https://github.com/thuml/timexer)]\n \n- [UniTS: Building a Unified Time Series Model](https://arxiv.org/abs/2403.00131)\n\n  - 29 Feb 2024, Shanghua Gao, et al.\n \n  - [[Official Code - UniTS](https://github.com/mims-harvard/UniTS)]\n\n- [TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis](https://arxiv.org/abs/2402.16412)\n\n  - 26 Feb 2024, Sabera Talukder, et al.\n \n  - [[Official Code - TOTEM](https://github.com/saberatalukder/totem)]\n\n- [LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting](https://arxiv.org/abs/2402.16132)\n\n  - 25 Feb 2024, Haoxin Liu, et al.\n \n  - [[Official Code - lstprompt](https://github.com/AdityaLab/lstprompt)]\n \n- [TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series](https://arxiv.org/abs/2308.08241)\n\n  - 22 Feb 2024, Chenxi Sun, et al.\n \n  - [[Official Code - TEST](https://github.com/scxsunchenxi/test)]\n \n- [CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting](https://arxiv.org/abs/2305.12095)\n\n  - 16 Feb 2024, Wang Xue, et al.\n \n  - [[Official Code - CARD](https://github.com/wxie9/card)]\n \n- [ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling](https://arxiv.org/abs/2402.10635)\n\n  - 16 Feb 2024, Yuqi Chen, et al.\n \n  - [[Official Code - ContiFormer](https://github.com/microsoft/SeqML/tree/main/ContiFormer)]\n \n- [Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review](https://arxiv.org/abs/2402.10350)\n\n  - 15 Feb 2024, Jing Su, et al.\n\n- [Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting](https://arxiv.org/abs/2310.08278)\n\n  - 08 Feb 2024, Kashif Rasul, et al.\n \n  - [[Official Code - lag-llama](https://github.com/time-series-foundation-models/lag-llama)]\n \n- [MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting](https://arxiv.org/abs/2311.18780)\n\n  - 08 Feb 2024, Linfeng Du, et al.\n \n- [MOMENT: A Family of Open Time-series Foundation Models](https://arxiv.org/abs/2402.03885)\n\n  - 06 Feb 2024, Mononito Goswami, et al.\n \n  - [[Official Code - MOMENT](https://anonymous.4open.science/r/BETT-773F/README.md)]\n  \n- [DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation](https://arxiv.org/abs/2402.06656)\n\n  - 05 Feb 2024, Yuan Gao, et al.\n \n- [Position Paper: What Can Large Language Models Tell Us about Time Series Analysis](https://arxiv.org/abs/2402.02713)\n\n  - 05 Feb 2024, Ming Jin, et al.\n \n- [AutoTimes: Autoregressive Time Series Forecasters via Large Language Models](https://arxiv.org/abs/2402.02370)\n\n  - 04 Feb 2024, Yong Liu, et al.\n \n  - [[Official Code - AutoTimes](https://github.com/thuml/AutoTimes)]\n\n- [FreDF: Learning to Forecast in Frequency Domain](https://arxiv.org/abs/2402.02399)\n\n  - 04 Feb 2024, Hao Wang, et al.\n \n  - [[Official Code - FreDF](https://github.com/master-plc/fredf)]\n\n- [Unified Training of Universal Time Series Forecasting Transformers](https://arxiv.org/abs/2402.02592)\n\n  - 04 Feb 2024, Gerald Woo, et al.\n \n  - [[Official Code - uni2ts](https://github.com/SalesforceAIResearch/uni2ts)]\n \n- [RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies](https://arxiv.org/abs/2402.02032)\n\n  - 03 Feb 2024, Hao Cheng, et al.\n \n  - [[Official Code - RobustTSF](https://github.com/haochenglouis/robusttsf)]\n \n- [Large Language Models for Time Series: A Survey](https://arxiv.org/abs/2402.01801)\n\n  - 02 Feb 2024, Xiyuan Zhang, et al.\n \n  - [[Official Code - awesome-llm-time-series](https://github.com/xiyuanzh/awesome-llm-time-series)]\n \n- [A Survey of Deep Learning and Foundation Models for Time Series Forecasting](https://arxiv.org/abs/2401.13912)\n\n  - 25 Jan 2024, John A. Miller, et al.\n \n- [LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters](https://arxiv.org/abs/2308.08469)\n\n  - 18 Jan 2024, Ching Chang, et al.\n\n- [MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting](https://arxiv.org/abs/2401.09261)\n\n  - 17 Jan 2024, Zongjiang Shang, et al.\n \n- [RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks](https://arxiv.org/abs/2401.09093)\n\n  - 17 Jan 2024, Haowen Hou, et al.\n  \n  - [[Official Code - RWKV-TS](https://github.com/howard-hou/RWKV-TS)]\n \n- [CNN Kernels Can Be the Best Shapelets](https://openreview.net/forum?id=O8ouVV8PjF)\n\n  - 16 Jan 2024, Eric Qu, et al.\n \n- [GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings](https://openreview.net/forum?id=c56TWtYp0W)\n\n  - 16 Jan 2024, Jingyun Xiao, et al.\n \n  - [[Official Code - GAFormer](https://github.com/nerdslab/GAFormer)]\n \n- [Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns](https://openreview.net/forum?id=CdjnzWsQax)\n\n  - 16 Jan 2024, Hongbin Huang, et al.\n \n- [Interpretable Sparse System Identification: Beyond Recent Deep Learning Techniques on Time-Series Prediction](https://openreview.net/forum?id=aFWUY3E7ws)\n\n  - 16 Jan 2024, Xiaoyi Liu, et al.\n \n- [Leveraging Generative Models for Unsupervised Alignment of Neural Time Series Data](https://openreview.net/forum?id=9zhHVyLY4K)\n\n  - 16 Jan 2024, Ayesha Vermani, et al.\n\n- [Self-Supervised Contrastive Learning for Long-term Forecasting](https://openreview.net/forum?id=nBCuRzjqK7)\n\n  - 16 Jan 2024, Junwoo Park, et al.\n \n  - [[Official Code - Self-Supervised-Contrastive-Forecsating](https://github.com/junwoopark92/Self-Supervised-Contrastive-Forecsating)]\n \n- [SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series](https://openreview.net/forum?id=s9z0HzWJJp)\n\n  - 16 Jan 2024, Junyan Cheng, et al.\n \n- [Transformer-Modulated Diffusion Models for Probabilistic Multivariate Time Series Forecasting](https://openreview.net/forum?id=qae04YACHs)\n\n  - 16 Jan 2024, Yuxin Li, et al.\n \n- [HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting](https://arxiv.org/abs/2401.05012)\n\n  - 10 Jan 2024, Shubao Zhao, et al.\n\n- [Universal Time-Series Representation Learning: A Survey](https://arxiv.org/abs/2401.03717)\n\n  - 08 Jan 2024, Patara Trirat, et al.\n \n  - [[Official Code - awesome-deep-time-series-representations](https://github.com/itouchz/awesome-deep-time-series-representations)]\n\n- [UnetTSF: A Better Performance Linear Complexity Time Series Prediction Model](https://arxiv.org/abs/2401.03001)\n\n  - 05 Jan 2024, Chu Li, et al.\n \n  - [[Official Code - UnetTSF](https://github.com/lichuustc/UnetTSF)]\n\n- [U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting](https://arxiv.org/abs/2401.02236)\n\n  - 04 Jan 2024, Xiang Ma, et al.\n \n  - [[Official Code - U-Mixer](https://github.com/XiangMa-Shaun/U-Mixer)]\n\n### 2023\n\n- [MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting](https://arxiv.org/abs/2401.00423)\n\n  - 31 Dec 2023, Wanlin Cai, et al.\n \n  - [[Official Code - MSGNet](https://github.com/yozhibo/msgnet)]\n \n- [Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting](https://arxiv.org/abs/2312.16790)\n\n  - 28 Dec 2023, Zhihao Yu, et al.\n \n  - [[Official Code - HMNet](https://github.com/yzhhoward/hmnet)]\n\n- [TSPP: A Unified Benchmarking Tool for Time-series Forecasting](https://arxiv.org/abs/2312.17100)\n\n  - 28 Dec 2023, Jan Bączek, et al.\n \n  - [[Official Code - TimeSeriesPredictionPlatform](https://github.com/NVIDIA/DeepLearningExamples/tree/master/Tools/PyTorch/TimeSeriesPredictionPlatform)]\n\n- [Continuous-time Autoencoders for Regular and Irregular Time Series Imputation](https://arxiv.org/abs/2312.16581)\n\n  - 27 Dec 2023, Hyowon Wi, et al.\n \n- [Learning to Embed Time Series Patches Independently](https://arxiv.org/abs/2312.16427)\n\n  - 27 Dec 2023, Seunghan Lee, et al.\n \n  - [[Official Code - pits](https://github.com/seunghan96/pits)]\n \n- [TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning](https://arxiv.org/abs/2312.15709)\n\n  - 25 Dec 2023, Jiexi Liu, et al.\n\n- [AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting](https://arxiv.org/abs/2312.13038)\n\n  - 20 Dec 2023, Raphael Fischer, et al.\n \n  - [[Official Code - xpcr](https://github.com/raphischer/xpcr)]\n\n- [CGS-Mask: Making Time Series Predictions Intuitive for All](https://arxiv.org/abs/2312.09513)\n\n  - 15 Dec 2023, Feng Lu, et al.\n \n- [Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting](https://arxiv.org/abs/2312.08763)\n\n  - 14 Dec 2023, Yanhong Li, et al.\n \n  - [[Official Code - DAN](https://github.com/davidanastasiu/dan)]\n\n- [SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation](https://arxiv.org/abs/2312.05790)\n\n  - 10 Dec 2023, Hyun Ryu, et al.\n \n  - [[Official Code - simpsi](https://github.com/hyun-ryu/simpsi)]\n \n- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752)\n\n  - 01 Dec 2023, Albert Gu, et al.\n \n  - [[Official Code - mamba](https://github.com/state-spaces/mamba)]\n \n- [Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting](https://arxiv.org/abs/2205.14415)\n  \n  - 24 Nov 2023, Yong Liu, et al.\n  \n  - [[Official Code - Nonstationary_Transformers](https://github.com/thuml/Nonstationary_Transformers)]\n\n- [FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective](https://arxiv.org/abs/2311.06190)\n\n  - 10 Nov 2023, Kun Yi, et al.\n \n  - [[Official Code - FourierGNN](https://github.com/aikunyi/fouriergnn)]\n \n- [Frequency-domain MLPs are More Effective Learners in Time Series Forecasting](https://arxiv.org/abs/2311.06184)\n\n  - 10 Nov 2023, Kun Yi, et al.\n \n  - [[Official Code - FreTS](https://github.com/aikunyi/frets)]\n\n- [Multi-resolution Time-Series Transformer for Long-term Forecasting](https://arxiv.org/abs/2311.04147)\n\n  - 07 Nov 2023, Yitian Zhang, et al.\n \n  - [[Official Code - MTST](https://github.com/networkslab/MTST)]\n \n- [PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning](https://arxiv.org/abs/2311.03768)\n\n  - 07 Nov 2023, Hao Liu, et al.\n \n- [BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis](https://arxiv.org/abs/2310.20496)\n\n  - 31 Oct 2023, Zelin Ni, et al.\n \n  - [[Official Code - Basisformer](https://github.com/nzl5116190/basisformer)]\n \n- [ProNet: Progressive Neural Network for Multi-Horizon Time Series Forecasting](https://arxiv.org/abs/2310.19322)\n\n  - 30 Oct 2023, Yang Lin\n \n- [Hierarchical Ensemble-Based Feature Selection for Time Series Forecasting](https://arxiv.org/abs/2310.17544)\n\n  - 26 Oct 2023, Ayşın Tümay, et al.\n \n- [Attention-Based Ensemble Pooling for Time Series Forecasting](https://arxiv.org/abs/2310.16231)\n\n  - 24 Oct 2023, Dhruvit Patel, et al.\n \n  - [[Official Code - denpool](https://github.com/awikner/denpool)]\n \n- [Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series](https://arxiv.org/abs/2310.13029)\n\n  - 19 Oct 2023, Ioannis Nasios, et al.\n \n  - [[Official Code - M5_Uncertainty_3rd_place](https://github.com/IoannisNasios/M5_Uncertainty_3rd_place)]\n \n- [A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis](https://arxiv.org/abs/2310.11959)\n\n  - 18 Oct 2023, Shuhan Zhong, et al.\n \n  - [[Official Code - MSD-Mixer](https://github.com/zshhans/msd-mixer)]\n\n- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://arxiv.org/abs/2310.10196)\n\n  - 16 Oct 2023, Ming Jin, et al.\n\n  - [[Official Code - awesome-timeseries-spatiotemporal-lm-llm](https://github.com/qingsongedu/awesome-timeseries-spatiotemporal-lm-llm)]\n \n- [UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting](https://arxiv.org/abs/2310.09751)\n\n  - 15 Oct 2023, Xu Liu, et al.\n \n  - [[Official Code - UniTime](https://github.com/liuxu77/unitime)]\n \n- [Counterfactual Explanations for Time Series Forecasting](https://arxiv.org/abs/2310.08137)\n\n  - 12 Oct 2023, Zhendong Wang, et al.\n \n  - [[Official Code - counterfactual-explanations-for-forecasting](https://github.com/zhendong3wang/counterfactual-explanations-for-forecasting)]\n \n- [Lag-Llama: Towards Foundation Models for Time Series Forecasting](https://arxiv.org/abs/2310.08278)\n\n  - 12 Oct 2023, Kashif Rasul, et al.\n \n  - [[Official Code - lag-llama](https://github.com/kashif/pytorch-transformer-ts/tree/main/lag-llama)]\n\n- [Large Language Models Are Zero-Shot Time Series Forecasters](https://arxiv.org/abs/2310.07820)\n\n  - 11 Oct 2023, Nate Gruver, et al.\n \n  - [[Official Code - llmtime](https://github.com/ngruver/llmtime)]\n \n- [Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain](https://arxiv.org/abs/2310.05063)\n\n  - 08 Oct 2023, Gerald Woo, et al.\n \n  - [[Official Code - pretrain-time-series-cloudops](https://github.com/salesforceairesearch/pretrain-time-series-cloudops)]\n \n- [Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs](https://arxiv.org/abs/2310.02619)\n\n  - 04 Oct 2023, Ilan Naiman, et al.\n \n- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://arxiv.org/abs/2310.01728)\n\n  - 03 Oct 2023, Ming Jin, et al.\n\n  - [[Official Code - Time-LLM](https://github.com/kimmeen/time-llm)]\n  \n- [Modality-aware Transformer for Time series Forecasting](https://arxiv.org/abs/2310.01232)\n\n  - 02 Oct 2023, Hajar Emami, et al.\n\n- [PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting](https://arxiv.org/abs/2310.00655)\n\n  - 01 Oct 2023, Zeying Gong, et al.\n \n  - [[Official Code - PatchMixer](https://github.com/Zeying-Gong/PatchMixer)]\n \n- [Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective](https://openreview.net/forum?id=5BqDSw8r5j)\n\n  - 22 Sep 2023, Zhiding Liu, et al.\n \n- [OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling](https://arxiv.org/abs/2309.12659)\n\n  - 22 Sep 2023, Yi-Fan Zhang, et al.\n \n  - [[Official Code - OneNet](https://github.com/yfzhang114/onenet)]\n \n- [WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting](https://arxiv.org/abs/2309.11319)\n\n  - 20 Sep 2023, Peiyuan Liu, et al.\n \n  - [[Official Code - WFTNet](https://github.com/Hank0626/WFTNet)]\n\n- [Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data](https://arxiv.org/abs/2309.05305)\n\n  - 11 Sep 2023, Yucheng Wang, et al.\n \n  - [[Official Code - FCSTGNN](https://github.com/Frank-Wang-oss/FCSTGNN)]\n \n- [PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series](https://arxiv.org/abs/2308.13703)\n\n  - 25 Aug 2023, Nicasia Beebe-Wang, et al.\n \n  - [[Official Code - irregular timeseries pretraining](https://github.com/google-research/google-research/tree/master/irregular_timeseries_pretraining)]\n \n- [TFDNet: Time-Frequency Enhanced Decomposed Network for Long-term Time Series Forecasting](https://arxiv.org/abs/2308.13386)\n\n  - 25 Aug 2023, Yuxiao Luo, et al.\n \n  - [[Official Code - TFDNet](https://github.com/YuxiaoLuo0013/TFDNet)]\n \n- [Easy attention: A simple self-attention mechanism for transformer-based time-series reconstruction and prediction](https://arxiv.org/abs/2308.12874)\n\n  - 24 Aug 2023, Marcial Sanchis-Agudo, et al.\n\n- [Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting](https://arxiv.org/abs/2308.11946)\n  \n  - 23 Aug 2023, Yifan Zhang, et al.\n\n- [SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting](https://arxiv.org/abs/2308.11200)\n  \n  - 22 Aug 2023, Shengsheng Lin, et al.\n \n  - [[Official Code - SegRNN](https://github.com/lss-1138/SegRNN)]\n\n- [LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs](https://arxiv.org/abs/2308.08469)\n  \n  - 16 Aug 2023, Ching Chang, et al.\n \n- [PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer](https://arxiv.org/abs/2308.04791)\n\n  - 09 Aug 2023, Shengsheng Lin, et al.\n \n- [DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction](https://arxiv.org/abs/2308.03274)\n\n  - 07 Aug 2023, Chengqing Yu, et al.\n\n- [Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3580305.3599378)\n  \n  - 04 Aug 2023, Arindam Jati, et al.\n\n- [Unsupervised Representation Learning for Time Series: A Review](https://arxiv.org/abs/2308.01578)\n  \n  - 03 Aug 2023, Qianwen Meng, et al.\n  \n  - [[Official Code - ULTS](https://github.com/mqwfrog/ULTS)]\n\n- [Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion](https://arxiv.org/abs/2308.01071)\n  \n  - 02 Aug 2023, Aurélien Renault, et al.\n  \n  - [[Official Code - Automatic-Feature-Engineering-for-TSC](https://github.com/aurelien-renault/Automatic-Feature-Engineering-for-TSC)]\n\n- [Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach](https://arxiv.org/abs/2308.01011)\n  \n  - 02 Aug 2023, Chunwei Yang, et al.\n  \n  - [[Official Code - Floss](https://github.com/agustdd/floss)]\n \n- [SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting](https://dl.acm.org/doi/10.14778/3611540.3611561)\n\n  - 01 Aug 2023, Yuanyuan Yao, et al.\n\n- [DeepTSF: Codeless machine learning operations for time series forecasting](https://arxiv.org/abs/2308.00709)\n  \n  - 28 Jul 2023, Sotiris Pelekis, et al.\n  \n  - [[Official Code - DeepTSF](https://github.com/I-NERGY/DeepTSF)]\n \n- [TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting](https://arxiv.org/abs/2307.14680)\n\n  - 27 Jul 2023, Nancy Xu, et al.\n \n  - [[Official Code - Time-GNN](https://github.com/xun468/Time-GNN)]\n\n- [TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers](https://arxiv.org/abs/2307.12667)\n  \n  - 24 Jul 2023, Md Fahim Sikder, et al.\n  \n  - [[Official Code - TransFusion](https://github.com/fahim-sikder/TransFusion)]\n \n- [Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting](https://arxiv.org/abs/2307.11494)\n\n  - 21 Jul 2023, Marcel Kollovieh, et al.\n \n  - [[Official Code - unconditional-time-series-diffusion](https://github.com/amazon-science/unconditional-time-series-diffusion)]\n \n- [TimeTuner: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual Explanations](https://arxiv.org/abs/2307.09916)\n\n  - 19 Jul 2023, Jianing Hao, et al.\n \n  - [[Official Code - TimeTuner](https://github.com/catherinehao/timetuner)]\n\n- [Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features](https://dl.acm.org/doi/abs/10.1145/3539618.3592013)\n  \n  - 18 July 2023, Seonmin Kim, et al.\n  \n  - [[Official Code - Look Ahead](https://github.com/sunsunmin/Look_Ahead)]\n \n- [GBT: Two-stage transformer framework for non-stationary time series forecasting](https://arxiv.org/abs/2307.08302)\n\n  - 17 Jul 2023, Li Shen, et al.\n  \n  - [[Official Code - GBT-Neural_Networks_2023](https://github.com/OrigamiSL/GBT-Neural_Networks_2023)]\n\n- [Sequential Monte Carlo Learning for Time Series Structure Discovery](https://arxiv.org/abs/2307.09607)\n  \n  - 13 Jul 2023, Feras A. Saad, et al.\n  \n  - [[Official Code - AutoGP.jl](https://github.com/probsys/AutoGP.jl)]\n\n- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://arxiv.org/abs/2307.03759)\n  \n  - 07 Jul 2023, Ming Jin, et al.\n  \n  - [[Official Code - Awesome-GNN4TS](https://github.com/kimmeen/awesome-gnn4ts)]\n \n- [GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting](https://arxiv.org/abs/2307.03595)\n\n  - 07 Jul 2023, Sitan Yang, et al.\n\n- [FITS: Modeling Time Series with 10k Parameters](https://arxiv.org/abs/2307.03756)\n\n  - 06 Jul 2023, Zhijian Xu, et al.\n \n  - [[Official Code - FITS](https://github.com/vewoxic/fits)]\n \n- [SAITS: Self-Attention-based Imputation for Time Series](https://arxiv.org/abs/2202.08516)\n  \n  - 05 Jul 2023, Wenjie Du, et al.\n  \n  - [[Official Code - SAITS](https://github.com/WenjieDu/SAITS)] \n\n- [SageFormer: Series-Aware Graph-Enhanced Transformers for Multivariate Time Series Forecasting](https://arxiv.org/abs/2307.01616)\n  \n  - 04 Jul 2023, Zhenwei Zhang, et al.\n\n- [ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection](https://arxiv.org/abs/2307.00754)\n  \n  - 03 Jul 2023, Yuhang Chen, et al.\n  \n  - [[Official Code - IMDiffusion](https://github.com/17000cyh/imdiffusion)]\n\n- [Precursor-of-Anomaly Detection for Irregular Time Series](https://arxiv.org/abs/2306.15489)\n  \n  - 27 Jun 2023, SheoYon Jhin, et al.\n  \n  - [[Official Code - PAD](https://github.com/sheoyon-jhin/PAD)]\n\n- [Anomaly Detection with Score Distribution Discrimination](https://arxiv.org/abs/2306.14403)\n  \n  - 26 Jun 2023, Minqi Jiang, et al.\n  \n  - [[Official Code - Overlap](https://github.com/Minqi824/Overlap)]\n\n- [InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/25845)\n\n  - 26 Jun 2023, Haizhou Cao, et al.\n  \n- [Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting](https://arxiv.org/abs/2306.11025)\n  \n  - 19 Jun 2023, Xinli Yu, et al.\n\n- [DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection](https://arxiv.org/abs/2306.10347)\n  \n  - 17 Jun 2023, Yiyuan Yang, et al.\n  \n  - [[Official Code - KDD2023-DCdetector](https://github.com/DAMO-DI-ML/KDD2023-DCdetector)]\n \n- [MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction](https://arxiv.org/abs/2306.10164)\n\n  - 16 Jun 2023, Iman Deznabi, et al.\n \n  - [[Official Code - MultiWave](https://github.com/information-fusion-lab-umass/multiwave)]\n\n- [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://arxiv.org/abs/2306.10125)\n  \n  - 16 Jun 2023, Kexin Zhang, et al.\n  \n  - [[Official Code - Awesome-SSL4TS](https://github.com/qingsongedu/Awesome-SSL4TS)]\n \n- [GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting](https://arxiv.org/abs/2306.08325)\n\n  - 14 Jun 2023, YanJun Zhao, et al.\n \n  - [[Official Code - GCformer](https://github.com/Yanjun-Zhao/GCformer)]\n\n- [TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting](https://arxiv.org/abs/2306.09364)\n  \n  - 14 Jun 2023, Vijay Ekambaram, et al.\n \n  - [[Official Code - tsfm](https://github.com/ibm/tsfm)]\n\n- [Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping](https://arxiv.org/abs/2306.06994)\n  \n  - 12 Jun 2023, Luxuan Wang, et al.\n\n- [Feature Programming for Multivariate Time Series Prediction](https://arxiv.org/abs/2306.06252)\n  \n  - 09 Jun 2023, Alex Reneau, et al.\n  \n  - [[Official Code - FeatureProgramming](https://github.com/SirAlex900/FeatureProgramming)]\n \n- [Self-Interpretable Time Series Prediction with Counterfactual Explanations](https://arxiv.org/abs/2306.06024)\n\n  - 09 Jun 2023, Jingquan Yan, et al.\n \n- [Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations](https://arxiv.org/abs/2306.05880)\n\n  - 09 Jun 2023, Etienne Le Naour, et al.\n\n- [Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency](https://arxiv.org/abs/2306.02109)\n\n  - 03 Jun 2023, Owen Queen, et al.\n\n- [An End-to-End Time Series Model for Simultaneous Imputation and Forecast](https://arxiv.org/abs/2306.00778)\n  \n  - 01 Jun 2023, Trang H. Tran, et al.\n \n- [Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context](https://arxiv.org/abs/2306.01112)\n\n  - 01 Jun 2023, Oussama Boussif, et al.\n \n  - [[Official Code - CrossViVit](https://github.com/gitbooo/CrossViVit)]\n\n- [Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting](https://arxiv.org/abs/2305.18838)\n\n  - 30 May 2023, Jiaxin Gao, et al.\n \n  - [[Official Code - Client](https://github.com/daxin007/client)]\n \n- [Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors](https://arxiv.org/abs/2305.18803)\n\n  - 30 May 2023, Yong Liu, et al.\n \n  - [[Official Code - Koopa](https://github.com/thuml/Koopa)]\n\n- [Learning Perturbations to Explain Time Series Predictions](https://arxiv.org/abs/2305.18840)\n  \n  - 30 May 2023, Joseph Enguehard.\n  \n  - [[Official Code - time_interpret](https://github.com/josephenguehard/time_interpret)]\n \n- [TLNets: Transformation Learning Networks for long-range time-series prediction](https://arxiv.org/abs/2305.15770)\n\n  - 25 May 2023, Wei Wang, et al.\n \n  - [[Official Code - TLNets](https://github.com/anonymity111222/tlnets)]\n\n- [A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting](https://arxiv.org/abs/2305.14649)\n\n  - 24 May 2023, Yushu Chen, et al.\n \n  - [[Official Code - JTFT](https://github.com/rationalspark/jtft)]\n \n- [Forecasting Irregularly Sampled Time Series using Graphs](https://arxiv.org/abs/2305.12932)\n\n  - 22 May 2023, Vijaya Krishna Yalavarthi, et al.\n \n  - [[Official Code - GraFITi](https://github.com/yalavarthivk/GraFITi)]\n\n- [Learning Structured Components: Towards Modular and Interpretable Multivariate Time Series Forecasting](https://arxiv.org/abs/2305.13036)\n  \n  - 22 May 2023, Jinliang Deng, et al.\n  \n  - [[Official Code - SCNN](https://github.com/KDDtest/SCNN)]\n \n- [Make Transformer Great Again for Time Series Forecasting: Channel Aligned Robust Dual Transformer](https://arxiv.org/abs/2305.12095)\n\n  - 20 May 2023, Wang Xue, et al.\n\n- [Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping](https://arxiv.org/abs/2305.10721)\n\n  - 18 May 2023, Zhe Li, et al.\n \n  - [[Official Code - RTSF](https://github.com/plumprc/rtsf)]\n \n- [How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?](https://arxiv.org/abs/2305.06587)\n\n  - 11 May 2023, Ming Jin, et al.\n\n- [IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers](https://arxiv.org/abs/2305.06741)\n\n  - 11 May 2023, Jingge Xiao, et al.\n \n  - [[Official Code - ivpvae](https://github.com/jingge326/ivpvae)]\n \n- [CUTS+: High-dimensional Causal Discovery from Irregular Time-series](https://arxiv.org/abs/2305.05890)\n\n  - 10 May 2023, Yuxiao Cheng, et al.\n\n  - [[Official Code - UNN](https://github.com/jarrycyx/unn)]\n\n- [Causal Discovery from Subsampled Time Series with Proxy Variables](https://arxiv.org/abs/2305.05276)\n\n  - 09 May 2023, Mingzhou Liu, et al.\n \n  - [[Official Code - proxy_causal_discovery](https://github.com/lmz123321/proxy_causal_discovery)]\n \n- [Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction](https://arxiv.org/abs/2305.08740)\n\n  - 09 May 2023, Sheng Xiang, et al.\n \n  - [[Official Code - THGNN](https://github.com/TongjiFinLab/THGNN)]\n  \n- [Mlinear: Rethink the Linear Model for Time-series Forecasting](https://arxiv.org/abs/2305.04800)\n\n  - 08 May 2023, Wei Li, et al.\n\n- [Diffusion Models for Time Series Applications: A Survey](https://arxiv.org/abs/2305.00624)\n  \n  - 01 May 2023, Lequan Lin, et al.\n\n- [Context Consistency Regularization for Label Sparsity in Time Series](https://openreview.net/forum?id=EvGOdASdHi)\n  \n  - 25 Apr 2023, Yooju Shin, et al.\n  \n  - [[Official Code - CrossMatch](https://github.com/kaist-dmlab/CrossMatch)]\n\n- [Prototype-oriented unsupervised anomaly detection for multivariate time series](https://openreview.net/forum?id=3vO4lS6PuF)\n  \n  - 25 Apr 2023, Yuxin Li, et al.\n  \n  - [[Official Code - PUAD](https://github.com/BoChenGroup/PUAD)]\n\n- [Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series](https://openreview.net/forum?id=WhRLdsDTBD)\n  \n  - 25 Apr 2023, Aniruddh Raghu, et al.\n  \n  - [[Official Code - SMD-SSL](https://github.com/aniruddhraghu/smd-ssl)]\n\n- [Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022](https://arxiv.org/abs/2305.04811)\n\n  - 21 Apr 2023, Cheng Zhang, et al.\n  \n- [Long-term Forecasting with TiDE: Time-series Dense Encoder](https://arxiv.org/abs/2304.08424)\n  \n  - 17 Apr 2023, Abhimanyu Das, et al.\n  \n  - [[Official Code - google-research - tide](https://github.com/google-research/google-research/tree/master/tide)] [[Unofficial Implementation - TiDE](https://github.com/lich99/TiDE)]\n\n- [Financial Time Series Forecasting using CNN and Transformer](https://arxiv.org/abs/2304.04912)\n  \n  - 11 Apr 2023, Zhen Zeng, et al.\n\n- [The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting](https://arxiv.org/abs/2304.05206)\n  \n  - 11 Apr 2023, Lu Han, et al.\n  \n  - [[Official Code](https://github.com/hanlu-nju/channel_independent_mtsf)]\n\n- [Handling Concept Drift in Global Time Series Forecasting](https://arxiv.org/abs/2304.01512)\n  \n  - 04 Apr 2023, Ziyi Liu, et al.\n  \n  - [[Official Code](https://github.com/Neal-Liu-Ziyi/Concept_Drift_Handling)]\n \n- [SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting](https://arxiv.org/abs/2303.18205)\n\n  - 31 Mar 2023, Xiaochen Zheng, et al.\n \n  - [[Official Code - SimTS_Representation_Learning](https://github.com/xingyu617/SimTS_Representation_Learning)]\n \n- [Towards Diverse and Coherent Augmentation for Time-Series Forecasting](https://arxiv.org/abs/2303.14254)\n\n  - 24 Mar 2023, Xiyuan Zhang, et al.\n\n- [UniTS: A Universal Time Series Analysis Framework with Self-supervised Representation Learning](https://arxiv.org/abs/2303.13804)\n  \n  - 24 Mar 2023, Zhiyu Liang, et al.\n  \n  - [[Official Code](https://github.com/LceOmlet/UniTS)]\n \n- [Conformal Prediction for Time Series with Modern Hopfield Networks](https://arxiv.org/abs/2303.12783)\n\n  - 22 Mar 2023, Andreas Auer, et al.\n \n  - [[Official Code - HopCPT](https://github.com/ml-jku/hopcpt)]\n\n- [Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning](https://arxiv.org/abs/2303.11716)\n  \n  - 21 Mar 2023, Dapeng Li, et al.\n\n- [Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting](https://arxiv.org/abs/2303.11000)\n  \n  - 20 Mar 2023, Terence L van Zyl.\n  \n  - [[Official Code](https://github.com/Pieter-Cawood/FFORMA-ESRNN)]\n \n- [Discovering Predictable Latent Factors for Time Series Forecasting](https://arxiv.org/abs/2303.10426)\n\n  - 18 Mar 2023, Jingyi Hou, et al.\n \n  - [[Official Code - discover_PLF](https://github.com/houjingyi-ustb/discover_plf)]\n \n- [TSMixer: An All-MLP Architecture for Time Series Forecasting](https://arxiv.org/abs/2303.06053)\n\n  - 10 Mar 2023, Si-An Chen, et al.\n \n  - [[Official Code - tsmixer](https://github.com/google-research/google-research/tree/master/tsmixer)]\n\n- [PHILNet: A novel efficient approach for time series forecasting using deep learning](https://www.sciencedirect.com/science/article/pii/S0020025523003183)\n\n  - 08 Mar 2023, M.J. Jiménez-Navarro, et al.\n \n  - [[Official Code - PHILNet](https://github.com/manjimnav/PHILNet)]\n\n- [Time Series Forecasting with Transformer Models and Application to Asset Management](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4375798)\n  \n  - 07 Mar 2023, Edmond Lezmi and Jiali Xu.\n \n- [Temporal Dependencies in Feature Importance for Time Series Predictions](https://arxiv.org/abs/2107.14317)\n\n  - 06 Mar 2023, Kin Kwan Leung, et al.\n \n  - [[Official Code - WinIT](https://github.com/layer6ai-labs/WinIT)]\n  \n- [Your time series is worth a binary image: machine vision assisted deep framework for time series forecasting](https://arxiv.org/abs/2302.14390)\n\n  - 28 Feb 2023, Luoxiao Yang, et al.\n\n  - [[Official Code - machine-vision-assisted-deep-time-series-analysis-MV-DTSA-](https://github.com/ikeyang/machine-vision-assisted-deep-time-series-analysis-mv-dtsa-)]\n \n- [LightCTS: A Lightweight Framework for Correlated Time Series Forecasting](https://arxiv.org/abs/2302.11974)\n\n  - 23 Feb 2023, Zhichen Lai, et al.\n \n  - [[Official Code - lightcts](https://github.com/ai4cts/lightcts)]\n \n- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://arxiv.org/abs/2302.11939)\n\n  - 23 Feb 2023, Tian Zhou, et al.\n \n  - [[Official Code - NeurIPS2023-One-Fits-All](https://github.com/DAMO-DI-ML/NeurIPS2023-One-Fits-All)]\n\n- [Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting](https://arxiv.org/abs/2302.14829)\n  \n  - 22 Feb 2023, Wei Fan, et al.\n  \n  - [[Official Code](https://github.com/weifantt/dish-ts)]\n\n- [FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification](https://arxiv.org/abs/2302.09818)\n  \n  - 20 Feb 2023, Mingyue Cheng, et al.\n  \n  - [[Official Code](https://github.com/Mingyue-Cheng/FormerTime)]\n \n- [FrAug: Frequency Domain Augmentation for Time Series Forecasting](https://arxiv.org/abs/2302.09292)\n\n  - 18 Feb 2023, Muxi Chen, et al.\n\n- [Improved Online Conformal Prediction via Strongly Adaptive Online Learning](https://arxiv.org/abs/2302.07869)\n  \n  - 15 Feb 2023, Aadyot Bhatnagar, et al.\n  \n  - [[Official Code](https://github.com/salesforce/online_conformal)]\n\n- [SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies](https://arxiv.org/abs/2302.05650)\n  \n  - 11 Feb 2023, Fan Zhou, et al.\n\n- [MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing](https://arxiv.org/abs/2302.04501)\n  \n  - 09 Feb 2023, Zhe Li, et al.\n\n  - [[Official Code - MTS-Mixers](https://github.com/plumprc/MTS-Mixers)]\n\n- [Domain Adaptation for Time Series Under Feature and Label Shifts](https://arxiv.org/abs/2302.03133)\n  \n  - 06 Feb 2023, Huan He, et al.\n  \n  - [[Official Code - Raincoat](https://github.com/mims-harvard/raincoat)]\n\n- [Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting](https://openreview.net/forum?id=vSVLM2j9eie)\n  \n  - 02 Feb 2023, Yunhao Zhang, Junchi Yan\n  \n  - [[Official Code - Crossformer](https://github.com/Thinklab-SJTU/Crossformer)]\n\n- [MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting](https://openreview.net/forum?id=zt53IDUR1U)\n  \n  - 02 Feb 2023, Huiqiang Wang, et al.\n  \n  - [[Official Code - MICN](https://github.com/wanghq21/MICN)]\n\n- [SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling](https://arxiv.org/abs/2302.00861)\n  \n  - 02 Feb 2023, Jiaxiang Dong, et al.\n \n  - [[Official Code - SimMTM](https://github.com/thuml/simmtm)]\n\n- PrimeNet : Pre-Training for Irregular Multivariate Time Series\n  \n  - [AAAI 2023](https://aaai.org/Conferences/AAAI-23/), Ranak Roy Chowdhury, et al.\n  \n  - [[Official Code](https://github.com/ranakroychowdhury/PrimeNet)]\n\n- [Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective](https://arxiv.org/abs/2301.11535)\n  \n  - 27 Jan 2023, Hui He, et al.\n\n- [Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text](https://arxiv.org/abs/2301.10887)\n\n  - 26 Jan 2023, Jinghui Liu, et al.\n\n- [Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series](https://arxiv.org/abs/2301.11308)\n  \n  - 26 Jan 2023, Abdul Fatir Ansari, et al.\n  \n  - [[Official Code - NCDSSM](https://github.com/clear-nus/NCDSSM)]\n \n- [Multi-view Kernel PCA for Time series Forecasting](https://arxiv.org/abs/2301.09811)\n\n  - 24 Jan 2023, Arun Pandey, et al.\n\n- [Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement](https://arxiv.org/abs/2301.03028)\n  \n  - 08 Jan 2023, Yan Li, et al.\n  \n  - [[Official Code](https://github.com/PaddlePaddle/PaddleSpatial/tree/main/research/D3VAE)]\n\n- [Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution](https://arxiv.org/abs/2301.02068)\n  \n  - 05 Jan 2023, Yan Li, et al.\n  \n  - [[Official Code](https://github.com/PaddlePaddle/PaddleSpatial/tree/main/research/Conformer)]\n\n- [Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem](https://arxiv.org/abs/2301.01772)\n\n  - 04 Jan 2023, Peiwang Tang, et al.\n  \n- [Neural SDEs for Conditional Time Series Generation and the Signature-Wasserstein-1 metric](https://arxiv.org/abs/2301.01315)\n  \n  - 03 Jan 2023, Pere Díaz Lozano, et al.\n  \n  - [[Official Code](https://github.com/pere98diaz/neural-sdes-for-conditional-time-series-generation-and-the-signature-wasserstein-1-metric)]\n\n### 2022\n\n- [End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation](https://arxiv.org/abs/2212.13706)\n  \n  - 28 Dec 2022, Shiyu Wang, et al.\n  \n  - [[Official Code](https://github.com/philipperemy/n-beats)]\n\n- [Dynamic Sparse Network for Time Series Classification: Learning What to \"see\"](https://arxiv.org/abs/2212.09840)\n  \n  - 19 Dec 2022, Qiao Xiao, et al.\n  \n  - [[Official Code](https://github.com/qiaoxiao7282/dsn)]\n\n- [Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting](https://arxiv.org/abs/2212.09030)\n  \n  - 18 Dec 2022, Slawek Smyl, et al.\n  \n  - [[Official Code](https://github.com/slaweks17/es-adrnn-with-context)]\n\n- [Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation](https://arxiv.org/abs/2212.08262)\n  \n  - 16 Dec 2022, Yizhou Dang, et al.\n  \n  - [[Official Code](https://github.com/kinggugu/ticoserec)]\n\n- [First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting](https://arxiv.org/abs/2212.08151)\n  \n  - 15 Dec 2022, Xiyuan Zhang, et al.\n  \n  - [[Code](https://github.com/xiyuanzh/gluonts/tree/tdformer/src/gluonts/nursery/TDformer)]\n\n- [Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series](https://arxiv.org/abs/2212.07771)\n  \n  - 15 Dec 2022, Nghia Duong-Trung, et al.\n  \n  - [[Official Code](https://github.com/duongtrung/time-series-temporal-saliency-patterns)]\n\n- Area2Area Forecasting: Looser Constraints, Better Predictions (Manuscript submitted to journal Information Sciences)\n  \n  - [[Official Code](https://github.com/OrigamiSL/A2A)]\n\n- [Sequential Predictive Conformal Inference for Time Series](https://arxiv.org/abs/2212.03463)\n  \n  - 07 Dec 2022, Chen Xu, et al.\n  \n  - [[Official Code - SPCI-code](https://github.com/hamrel-cxu/SPCI-code)]\n\n- [A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting](https://arxiv.org/abs/2212.02789)\n  \n  - 06 Dec 2022, Zanwei Zhou, et al.\n  \n  - [[Official Code](https://github.com/Zanue/MTSF_TVT)]\n\n- [DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting](https://proceedings.mlr.press/v162/lan22a.html)\n  \n  - 06 Dec 2022, Shiyong Lan, et al.\n  \n  - [[Official Code](https://github.com/SYLan2019/DSTAGNN)]\n\n- [Learning of Cluster-based Feature Importance for Electronic Health Record Time-series](https://proceedings.mlr.press/v162/aguiar22a.html)\n  \n  - 06 Dec 2022, Henrique Aguiar, et al.\n  \n  - [[Official Code](https://github.com/hrna-ox/camelot-icml)]\n\n- [CoTMix: Contrastive Domain Adaptation for Time-Series via Temporal Mixup](https://arxiv.org/abs/2212.01555)\n  \n  - 03 Dec 2022, Emadeldeen Eldele, et al.\n  \n  - [[Official Code - CoTMix](https://github.com/emadeldeen24/cotmix)]\n\n- [FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting](https://arxiv.org/abs/2212.01209)\n  \n  - 02 Dec 2022, Maowei Jiang, et al.\n  \n  - [[Official Code](https://github.com/zero-coder/fecam)]\n\n- [MHCCL: Masked Hierarchical Cluster-wise Contrastive Learning for Multivariate Time Series](https://arxiv.org/abs/2212.01209)\n  \n  - 02 Dec 2022, Qianwen Meng, et al.\n  \n  - [[Official Code](https://github.com/mqwfrog/mhccl)]\n \n- [CRU: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data](https://arxiv.org/abs/2211.16653)\n\n  - 30 Nov 2022, Sunghyun Sim, et al.\n\n* [AirFormer: Predicting Nationwide Air Quality in China with Transformers](https://arxiv.org/abs/2211.15979)\n  \n  * 29 Nov 2022, Yuxuan Liang, et al.\n  \n  * [[Official Code](https://github.com/yoshall/airformer)]\n\n* [Learning Latent Seasonal-Trend Representations for Time Series Forecasting](https://nips.cc/Conferences/2022/Schedule?showEvent=55179)\n  \n  * 29 Nov 2022, Zhiyuan Wang, et al.\n  \n  * [[Official Code](https://github.com/zhycs/LaST)]\n\n* [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/abs/2211.14730)\n  \n  * 27 Nov 2022, Yuqi Nie, et al.\n  \n  * [[Official Code](https://github.com/yuqinie98/PatchTST)]\n\n* [A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting](https://arxiv.org/abs/2211.02989)\n  \n  * 05 Nov 2022, Aryan Jadon, et al.\n  \n  * [[Official Code](https://github.com/aryan-jadon/regression-loss-functions-in-time-series-forecasting-tensorflow)]\n\n* [Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion](https://arxiv.org/abs/2211.02590)\n\n  * 04 Nov 2022, Marin Biloš, et al.\n\n* [Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks](https://openreview.net/forum?id=pMumil2EJh)\n  \n  * 01 Nov 2022, Yijing Liu, et al.\n\n* [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)\n  \n  * 01 Nov 2022, Yuzhou Chen, et al.\n\n* [TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting](https://arxiv.org/abs/2210.15050)\n  \n  * 26 Oct 2022, Hyunwook Lee, et al.\n- [WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting](https://arxiv.org/abs/2210.14303)\n  \n  - 25 Oct 2022, Youngin Cho, et al.\n  \n  - [[Official Code](https://github.com/choyi0521/WaveBound)]\n \n- [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://arxiv.org/abs/2106.09305)\n\n  - 13 Oct 2022, Minhao Liu, et al\n \n  - [[Official Code - SCINet](https://github.com/cure-lab/SCINet)]\n\n- [Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts](https://arxiv.org/abs/2210.03675)\n  \n  - 07 Oct 2022, Rui Wang, et al.\n  \n  - [[Official Code](https://github.com/google-research/google-research/tree/master/KNF)]\n\n- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://arxiv.org/abs/2210.02186)\n  \n  - 05 Oct 2022, Haixu Wu, et al.\n  \n  - [[Official Code](https://github.com/thuml/timesnet)]\n* [Retrieval Based Time Series Forecasting](https://arxiv.org/abs/2209.13525)\n  \n  * 27 Sep 2022, Baoyu Jing, et al.\n\n* [FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting](https://openreview.net/forum?id=WXjBX7uz7lO)\n  \n  * 22 Sep 2022, Li Shen, et al.\n  \n  * [[Official Code](https://github.com/OrigamiSL/FDNet)]\n \n* [PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting](https://arxiv.org/abs/2210.08964)\n\n  * 20 Sep 2022, Hao Xue, et al.\n \n  * [[Official Code - PISA](https://github.com/haounsw/pisa)]\n\n* [Out-of-Distribution Representation Learning for Time Series Classification](https://arxiv.org/abs/2209.07027)\n  \n  * 15 Sep 2022, Wang Lu, et al.\n  \n  * [[Official Code](https://github.com/microsoft/robustlearn/tree/main/diversify)]\n\n* [Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward](https://www.tandfonline.com/doi/full/10.1080/01605682.2022.2118629)\n  \n  * 05 Sep 2022, Spyros Makridakis, et al.\n\n* [Expressing Multivariate Time Series as Graphs with Time Series Attention Transformer](https://arxiv.org/abs/2208.09300)\n  \n  * 19 Aug 2022, William T. Ng, et al.\n  \n  * [[Official Code](https://github.com/radiantresearch/tsat)]\n\n* [Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting](https://dl.acm.org/doi/10.1145/3534678.3539396)\n  \n  * 14 Aug 2022, Zezhi Shao, et al.\n  \n  * [[Official Code]](https://github.com/zezhishao/STEP)\n\n* [Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting](https://arxiv.org/abs/2208.05233)\n  \n  * 10 Aug 2022, Zezhi Shao, et al.\n  \n  * [[Official Code](https://github.com/zezhishao/stid)]\n\n* [Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect](https://arxiv.org/abs/2207.10941)\n  \n  * 22 Jul 2022, Li Shen, et al.\n  \n  * [[Official Code](https://github.com/OrigamiSL/RTNet2022)]\n\n* [Formal Algorithms for Transformers](https://arxiv.org/abs/2207.09238)\n  \n  * 19 Jul 2022, Mary Phuong, Marcus Hutter\n\n* [Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms](https://arxiv.org/abs/2207.09572)\n  \n  * 19 Jul 2022, Linbo Liu, et al.\n  \n  * [[Official Code - gluonts](https://github.com/awslabs/gluonts)]\n \n* [Generalizable Memory-driven Transformer for Multivariate Long Sequence Time-series Forecasting](https://arxiv.org/abs/2207.07827)\n\n  * 16 Jul 2022, Xiaoyun Zhao, et al.\n \n  * [[Official Code - CLMFormer](https://github.com/mlii0117/CLMFormer)]\n\n* [Learning Deep Time-index Models for Time Series Forecasting](https://arxiv.org/abs/2207.06046)\n  \n  * 13 Jul 2022, Gerald Woo, et al.\n  \n  * [[Official Code - DeepTime](https://github.com/salesforce/deeptime)]\n\n* [Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes](https://arxiv.org/abs/2207.06544)\n  \n  - 13 Jul 2022, Gregory Benton, et al.\n  \n  - [[Official Code](https://github.com/g-benton/Volt)]\n\n- [Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures](https://arxiv.org/abs/2207.01186)\n  \n  - 04 Jul 2022, Tianping Zhang, et al.\n\n- [CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/20320)\n  \n  - 28 Jun 2022, Hui He, et al.\n\n- [Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting](https://arxiv.org/abs/2206.13816)\n  \n  - 28 Jun 2022, Junchen Ye, et al\n  \n  - [[Official Code - ESG](https://github.com/liuzh-19/esg)]\n\n- [Utilizing Expert Features for Contrastive Learning of Time-Series Representations](https://arxiv.org/abs/2206.11517)\n  \n  - 23 Jun 2022, Manuel Nonnenmacher, et al.\n  \n  - [[Official Code](https://github.com/boschresearch/expclr)]\n\n- [Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://arxiv.org/abs/2206.08496)\n  \n  - 17 Jun 2022, Xiang Zhang, et al.\n  \n  - [[Official Code](https://github.com/mims-harvard/tfc-pretraining)]\n\n- [Closed-Form Diffeomorphic Transformations for Time Series Alignment](https://arxiv.org/abs/2206.08107)\n  \n  - 16 Jun 2022, Iñigo Martinez, et al.\n  \n  - [[Official Code](https://github.com/imartinezl/difw)]\n\n- [Contrastive Learning for Unsupervised Domain Adaptation of Time Series](https://arxiv.org/abs/2206.06243)\n  \n  - 13 Jun 2022, Yilmazcan Ozyurt, et al.\n  \n  - [[Official Code](https://github.com/oezyurty/cluda)]\n\n- [Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting](https://arxiv.org/abs/2206.04038)\n  \n  - 08 Jun 2022, Amin Shabani, et al.\n  \n  - [[Official Code](https://github.com/Scaleformer/Scaleformer)]\n\n- [SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series](https://arxiv.org/abs/2205.15875)\n  \n  - 31 May 2022, Iris A.M. Huijben, et al.\n  \n  - [[Official Code - SOM-CPC](https://github.com/iamhuijben/som-cpc)]\n\n- [Are Transformers Effective for Time Series Forecasting?](https://arxiv.org/abs/2205.13504)\n  \n  - 26 May 2022, Ailing Zeng, et al.\n  \n  - [[Official Code - LTSF-Linear](https://github.com/cure-lab/LTSF-Linear)]\n\n- [FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting](https://arxiv.org/abs/2205.08897)\n  \n  - 18 May 2022, Tian Zhou, et al.\n  \n  - [[Official Code](https://github.com/tianzhou2011/FiLM/)]\n\n- [Efficient Automated Deep Learning for Time Series Forecasting](https://arxiv.org/abs/2205.05511)\n  \n  - 11 May 2022, Difan Deng, et al.\n  \n  - [[Official Code](https://github.com/automl/Auto-PyTorch)]\n \n- [Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version](https://arxiv.org/abs/2204.13767) [[An introduction](https://ccloud0525.github.io/en/Triformer/)]\n\n  - 28 Apr 2022, Razvan-Gabriel Cirstea, et al.\n\n- [EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting](https://dl.acm.org/doi/abs/10.1145/3485447.3512030)\n  \n  - 25 Apr 2022, Sheo Yon Jhin, et al.\n  \n  - [[Official Code](https://github.com/sheoyon-jhin/EXIT)]\n\n- [Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction](https://dl.acm.org/doi/abs/10.1145/3485447.3512056)\n  \n  - 25 Apr 2022, Min Hou, et al.\n\n- [RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph](https://dl.acm.org/doi/abs/10.1145/3485447.3511974)\n  \n  - 25 Apr 2022, Ruijie Wang, et al.\n  \n  - [[Official Code](https://github.com/DiMarzioBian/RETE_TheWebConf)]\n\n- [A data filling methodology for time series based on CNN and (Bi)LSTM neural networks](https://arxiv.org/abs/2204.09994)\n\n  - 21 Apr 2022, Kostas Tzoumpas, et al.\n\n- [ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data](https://arxiv.org/abs/2203.08321)\n  \n  - 15 Mar 2022, Mohamed Ragab, et al.\n  \n  - [[Official Code](https://github.com/emadeldeen24/AdaTime)]\n\n- [DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting](https://arxiv.org/abs/2203.07681)\n  \n  - 15 Mar 2022, Wei Fan, et al.\n  \n  - [[Official Code](https://github.com/weifantt/depts)]\n\n- [Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting](https://arxiv.org/abs/2202.11356)\n  \n  - 23 Feb 2022, Dazhao Du, et al.\n  \n  - [[Code](https://github.com/ddz16/Preformer)]\n\n- [Adaptive Conformal Predictions for Time Series](https://arxiv.org/abs/2202.07282)\n  \n  - 15 Feb 2022, Margaux Zaffran, et al.\n  \n  - [[Official Code](https://github.com/mzaffran/adaptiveconformalpredictionstimeseries)]\n\n- [ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction](https://dl.acm.org/doi/10.1145/3488560.3498444)\n  \n  - 15 Feb 2022, Liang Zhao, et al.\n  \n  - [[Official Code](https://github.com/k51/STGSP)]\n\n- [Transformers in Time Series: A Survey](https://arxiv.org/abs/2202.07125)\n  \n  - 15 Feb 2022, Qingsong Wen, et al.\n  \n  - [[Official Code](https://github.com/qingsongedu/time-series-transformers-review)]\n\n- [TACTiS: Transformer-Attentional Copulas for Time Series](https://arxiv.org/abs/2202.03528)\n  \n  - 7 Feb 2022, Alexandre Drouin, et al.\n  \n  - [[Official Code - TACTiS](https://github.com/ServiceNow/tactis)]\n\n- [CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting](https://arxiv.org/abs/2202.01575)\n  \n  - 03 Feb 2022, Gerald Woo, et al.\n  \n  - [[Official Code - CoST](https://github.com/salesforce/CoST)]\n\n- [ETSformer: Exponential Smoothing Transformers for Time-series Forecasting](https://arxiv.org/abs/2202.01381)\n  \n  - 03 Feb 2022, Gerald Woo, et al.\n  \n  - [[Official Code - ETSformer](https://github.com/salesforce/ETSformer)]\n\n- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://arxiv.org/abs/2201.12740)\n  \n  - 30 Jan 2022, Tian Zhou, et al.\n  \n  - [[Official Code - FEDformer](https://github.com/MAZiqing/FEDformer)]\n\n- [N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting](https://arxiv.org/abs/2201.12886)\n  \n  - 30 Jan 2022, Cristian Challu, et al.\n  \n  - [[Official Code - n-hits](https://github.com/cchallu/n-hits)]\n\n- [Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift](https://openreview.net/forum?id=cGDAkQo1C0p)\n  \n  - 29 Jan 2022, Taesung Kim, et al.\n  \n  - [[Official Code - RevIN](https://github.com/ts-kim/RevIN)]\n \n- [Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting](https://arxiv.org/abs/2201.04828)\n\n  - 13 Jan 2022, Ling Chen, et al.\n \n  - [[Official Code - MAGNN](https://github.com/shangzongjiang/magnn)]\n\n### 2021\n\n- [AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version](https://arxiv.org/abs/2112.11174)\n\n  - 21 Dec 2021, Xinle Wu, et al.\n \n  - [[Official Code - AutoCTS](https://github.com/decisionintelligence/AutoCTS)]\n\n- [A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models](https://arxiv.org/abs/2112.09293)\n  \n  - 17 Dec 2021, Saroj Gopali, et al.\n\n- [TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs](https://arxiv.org/abs/2112.08025)\n  \n  - 15 Dec 2021, Yushan Liu, et al.\n  \n  - [[Official Code - TLogic](https://github.com/liu-yushan/tlogic)]\n\n- [Parameter Efficient Deep Probabilistic Forecasting](https://arxiv.org/abs/2112.02905)\n\n  - 14 Dec 2021, Olivier Sprangers, et al.\n \n  - [[Official Code - PEDPF](https://github.com/elephaint/pedpf)]\n\n- [NeuralProphet: Explainable Forecasting at Scale](https://arxiv.org/abs/2111.15397)\n  \n  - 29 Nov 2021, Oskar Triebe, et al.\n  \n  - [[Official Code - NeuralProphet](https://github.com/ourownstory/neural_prophet)]\n\n- [Modeling Irregular Time Series with Continuous Recurrent Units](https://arxiv.org/abs/2111.11344)\n  \n  - 22 Nov 2021, Mona Schirmer, et al.\n  \n  - [[Official Code - Continuous-Recurrent-Units](https://github.com/boschresearch/continuous-recurrent-units)]\n\n- [Transferable Time-Series Forecasting under Causal Conditional Shift](https://arxiv.org/abs/2111.03422)\n\n  - 05 Nov 2021, Zijian Li, et al.\n \n  - [[Official Code - GCA](https://github.com/dmirlab-group/gca)]\n\n- [Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series](https://arxiv.org/abs/2111.02922)\n  \n  - 04 Nov 2021, Daniel Kramer, et al.\n  \n  - [[Official Code - mmPLRNN](https://github.com/durstewitzlab/mmplrnn)]\n\n- [ClaSP - Time Series Segmentation](https://dl.acm.org/doi/abs/10.1145/3459637.3482240)\n  \n  - 30 Oct 2021, Patrick Schäfer, et al.\n  \n  - [[Official Code](https://github.com/ermshaua/time-series-segmentation-benchmark)]\n \n- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://arxiv.org/abs/2110.13716)\n  \n  - 26 Oct 2021, Wentao Xu, et al.\n  \n  - [[Official Code](https://github.com/wentao-xu/hist)]\n\n- [Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting](https://arxiv.org/abs/2110.08255)\n  \n  - 13 Oct 2021, Kiran Madhusudhanan, et al.\n  \n  - [[Official Code](https://github.com/18kiran12/Yformer-Time-Series-Forecasting)]\n\n- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://arxiv.org/abs/2110.02642)\n  \n  - 06 Oct 2021, Jiehui Xu, et al.\n  \n  - [[Official Code - Anomaly-Transformer](https://github.com/thuml/Anomaly-Transformer)]\n\n- [CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning](https://arxiv.org/abs/2109.14778)\n  \n  - 30 Sep 2021, Garrett Wilson, et al.\n  \n  - [[Official Code](https://github.com/floft/calda)]\n\n- [Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting](https://openreview.net/forum?id=0EXmFzUn5I)\n  \n  - 29 Sep 2021, Shizhan Liu, et al.\n  \n  - [[Code](https://github.com/alipay/Pyraformer)]\n \n- [Long-Range Transformers for Dynamic Spatiotemporal Forecasting](https://arxiv.org/abs/2109.12218)\n\n  - 24 Sep 2021, Jake Grigsby, et al\n \n  - [[Official Code - spacetimeformer](https://github.com/qdata/spacetimeformer)]\n\n- [DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications](https://arxiv.org/abs/2109.11495)\n  \n  - 23 Sep 2021, Dongqi Han, et al.\n  \n  - [[Official Code](https://github.com/dongtsi/deepaid)]\n\n- [CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting](https://arxiv.org/abs/2109.07438)\n  \n  - 15 Sep 2021, Harshavardhan Kamarthi, et al.\n  \n  - [[Official Code](https://github.com/adityalab/camul)]\n\n- [Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation](https://arxiv.org/abs/2109.04871)\n  \n  - 10 Sep 2021, Ziluo Ding, et al.\n  \n  - [[Official Code](https://github.com/ruizhao26/ste-flownet)]\n\n- [TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting](https://arxiv.org/abs/2108.12784)\n  \n  - 29 Aug 2021, Li Shen, Yangzhu Wang\n  \n  - [[Official Code](https://github.com/OrigamiSL/TCCT2021-Neurocomputing-)]\n\n- [Machine learning in the Chinese stock market](https://www.sciencedirect.com/science/article/pii/S0304405X21003743?via%3Dihu)\n  \n  - 27 Aug 2021, Markus Leippold, et al.\n\n- [Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization](https://dl.acm.org/doi/10.1145/3447548.3467174)\n  \n  - 14 Aug 2021, Ahmed Abdulaal, et al.\n  \n  - [[Official Code](https://github.com/eBay/RANSynCoders)]\n\n- [AdaRNN: Adaptive Learning and Forecasting of Time Series](https://arxiv.org/abs/2108.04443)\n  \n  - 10 Aug 2021, Yuntao Du, et al.\n  \n  - [[Official Code](https://github.com/jindongwang/transferlearning)]\n \n- [CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation](https://arxiv.org/abs/2107.03502)\n\n  - 07 Jul 2021, Yusuke Tashiro, et al.\n\n  - [[Official Code - CSDI](https://github.com/ermongroup/csdi)]\n \n- [Spatiotemporal information conversion machine for time-series prediction](https://arxiv.org/abs/2107.01353)\n\n  - 03 Jul 2021, Hao Peng, et al.\n \n  - [[Official Code - STICM](https://github.com/mahp-scut/sticm)]\n\n- [Time-Series Representation Learning via Temporal and Contextual Contrasting](https://arxiv.org/abs/2106.14112)\n  \n  - 26 Jun 2021, Emadeldeen Eldele, et al.\n  \n  - [[Official Code](https://github.com/emadeldeen24/TS-TCC)]\n\n- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008)\n  \n  - 24 Jun 2021, Haixu Wu, et al.\n  \n  - [[Code](https://github.com/thuml/Autoformer)]\n\n- [TS2Vec: Towards Universal Representation of Time Series](https://arxiv.org/abs/2106.10466)\n  \n  - 19 Jun 2021, Zhihan Yue, et al.\n  \n  - [[Code](https://github.com/yuezhihan/ts2vec)]\n\n- [ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models](https://arxiv.org/abs/2106.10121)\n  \n  - 18 Jun 2021, Tijin Yan, et al.\n  \n  - [[Official Code](https://github.com/yantijin/ScoreGradPred)]\n\n- [Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction](https://arxiv.org/abs/2106.09305)\n  \n  - 17 Jun 2021, Minhao Liu, et al. \n  \n  - [[Code](https://github.com/cure-lab/SCINet)]\n\n- [Voice2Series: Reprogramming Acoustic Models for Time Series Classification](https://arxiv.org/abs/2106.09296)\n  \n  - 17 Jun 2021, Chao-Han Huck Yang, et al.\n  \n  - [[Official Code](https://github.com/huckiyang/Voice2Series-Reprogramming)]\n\n- [Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding](https://arxiv.org/abs/2106.00750)\n  \n  - 01 Jun 2021, Sana Tonekaboni, et al.\n  \n  - [[Official Code](https://github.com/sanatonek/TNC_representation_learning)]\n\n- [Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting](https://arxiv.org/abs/2105.04100)\n  \n  - 10 May 2021, Yuzhou Chen, et al.\n  \n  - [[Official Code](https://github.com/Z-GCNETs/Z-GCNETs)]\n\n- [Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx](https://arxiv.org/abs/2104.05522)\n  \n  - 12 Apr 2021, Kin G. Olivares, et al.\n  \n  - [[Code](https://github.com/cchallu/nbeatsx)]\n \n- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://arxiv.org/abs/2103.12057)\n\n  - 22 Mar 2021, Pedro Lara-Benítez, et al.\n \n  - [[Official Code - TimeSeriesForecasting-DeepLearning](https://github.com/pedrolarben/TimeSeriesForecasting-DeepLearning)]\n\n- [Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting](https://arxiv.org/abs/2103.07719)\n  \n  - 13 Mar 2021, Defu Cao, et al.\n  \n  - [[Official Code](https://github.com/microsoft/StemGNN)]\n\n- [FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection](https://dl.acm.org/doi/10.1145/3437963.3441823)\n  \n  - 08 Mar 2021, Jia Li, et al.\n  \n  - [[Official Code](https://github.com/jlidw/FluxEV)]\n\n- [Perceiver: General Perception with Iterative Attention](https://arxiv.org/abs/2103.03206)\n  \n  - 04 Mar 2021, Andrew Jaegle, et al.\n  \n  - [[Official Code](https://github.com/deepmind/deepmind-research/tree/master/perceiver)] [[Community Code](https://github.com/lucidrains/perceiver-pytorch)]\n\n- [Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series](https://arxiv.org/abs/2103.02164)\n  \n  - 03 Mar 2021, Yinjun Wu, et al.\n  \n  - [[Official Code](https://github.com/thuwuyinjun/DGM2)]\n\n- [Domain Adaptation for Time Series Forecasting via Attention Sharing](https://arxiv.org/abs/2102.06828)\n  \n  - 13 Feb 2021, Xiaoyong Jin, et al.\n  \n  - [[Official Code](https://github.com/DMIRLAB-Group/SASA)]\n\n- [Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting](https://arxiv.org/abs/2102.00431)\n  \n  - 31 Jan 2021, Longyuan Li, et al.\n  \n  - [[Official Code](https://github.com/longyuanli/VSMHN)]\n\n- [Adjusting for Autocorrelated Errors in Neural Networks for Time Series](https://arxiv.org/abs/2101.12578)\n  \n  - 28 Jan 2021, Fan-Keng Sun, et al.\n  \n  - [[Official Code](https://github.com/Daikon-Sun/AdjustAutocorrelation)]\n\n- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://arxiv.org/abs/2101.12072)\n  \n  - 28 Jan 2021, Kashif Rasul, et al.\n  \n  - [[Official Code - pytorch-ts](https://github.com/zalandoresearch/pytorch-ts)]\n\n- [Long Horizon Forecasting With Temporal Point Processes](https://arxiv.org/abs/2101.02815)\n  \n  - 08 Jan 2021, Prathamesh Deshpande, et al.\n  \n  - [[Official Code](https://github.com/pratham16cse/DualTPP)]\n\n- [Do We Really Need Deep Learning Models for Time Series Forecasting?](https://arxiv.org/abs/2101.02118)\n  \n  - 06 Jan 2021, Shereen Elsayed, et al.\n  \n  - [[Code](https://github.com/Daniela-Shereen/GBRT-for-TSF)]\n\n- [Conditional Local Convolution for Spatio-temporal Meteorological Forecasting](https://arxiv.org/abs/2101.01000)\n  \n  - 04 Jan 2021, Haitao Lin, et al.\n  \n  - [[Official Code](https://github.com/BIRD-TAO/CLCRN)]\n\n### 2020\n\n* [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436)\n  \n  * 14 Dec 2020, Haoyi Zhou, et al.\n  \n  * [[Code](https://github.com/zhouhaoyi/Informer2020)]\n\n* [TimeSHAP: Explaining Recurrent Models through Sequence Perturbations](https://arxiv.org/abs/2012.00073)\n  \n  * 30 Nov 2020, João Bento, et al.\n  \n  * [[Official Code](https://github.com/feedzai/timeshap)]\n\n* [Conformal prediction for time series](https://arxiv.org/abs/2010.09107)\n  \n  * 18 Oct 2020, Chen Xu, et al.\n  \n  * [[Official Code - EnbPI](https://github.com/hamrel-cxu/EnbPI)]\n\n* [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://arxiv.org/abs/2010.02803)\n  \n  * 06 Oct 2020, George Zerveas, et al.\n  \n  * [[Code](https://github.com/gzerveas/mvts_transformer)]\n\n* [Deep Sw","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDaoSword%2FTime-Series-Forecasting-and-Deep-Learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FDaoSword%2FTime-Series-Forecasting-and-Deep-Learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDaoSword%2FTime-Series-Forecasting-and-Deep-Learning/lists"}