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https://github.com/songqiang321/time-series-papers

This is a repository for collecting papers and code in time series domain.
https://github.com/songqiang321/time-series-papers

ai-for-time-series data-science deep-learning llm-for-time-series machine-learning time-series time-series-analysis time-series-forecasting time-series-prediction transformer

Last synced: 5 months ago
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This is a repository for collecting papers and code in time series domain.

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README 

          
# Time-Series-Papers

This is a repository for collecting papers and code in time series domain.



## Table of Content  



- [Linear](#linear)

- [RNN and CNN](#rnn-and-cnn)

- [Transformer](#transformer)

- [GNN](#gnn)

- [LLM Framework](#llm-framework)

- [Diffusion Model](#diffusion-model)

- [Benchmark and Dataset](#benchmark-and-dataset)

- [Repositories](#repositories)



```bash

  ├─ Linear/  

  ├─ RNN and CNN/           

  ├─ Transformer/

  ├─ GNN/

  ├─ LLM Framework/

  ├─ Diffusion Model/

  ├─ Benchmark and Dataset/                      

  └─ Repositories/         

```



---



## Linear



- **N-BEATS: Neural basis expansion analysis for interpretable time series forecasting**, _Oreshkin et al._, ICLR 2020. \[[paper](https://arxiv.org/abs/1905.10437)\]\[[n-beats](https://github.com/philipperemy/n-beats)\]\[[N-BEATS](https://github.com/ServiceNow/N-BEATS)\]

- DLinear: **Are Transformers Effective for Time Series Forecasting**, _Zeng et al._, AAAI 2023. \[[paper](https://arxiv.org/abs/2205.13504)\]\[[code](https://github.com/cure-lab/LTSF-Linear)\]\[[DiPE-Linear](https://github.com/wintertee/DiPE-Linear)\]\[[TimeLinear](https://github.com/zclzcl0223/TimeLinear)\]

- **TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting**, _Ekambaram et al._, KDD 2023. \[[paper](https://arxiv.org/abs/2306.09364)\]\[[model](https://huggingface.co/docs/transformers/main/en/model_doc/patchtsmixer)\]\[[example](https://github.com/ibm/tsfm#notebooks-links)\]

- FreTS: **Frequency-domain MLPs are More Effective Learners in Time Series Forecasting**, _Yi et al._, NeurIPS 2023. \[[paper](https://arxiv.org/abs/2311.06184)\]\[[code](https://github.com/aikunyi/FreTS)\]\[[FilterNet](https://github.com/aikunyi/FilterNet)\]

- **Tiny Time Mixers (TTMs): Fast Pretrained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series**, _Ekambaram et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2401.03955)\]\[[code](https://github.com/ibm-granite/granite-tsfm/tree/main/tsfm_public/models/tinytimemixer)\]

- **FCDNet: Frequency-Guided Complementary Dependency Modeling for Multivariate Time-Series Forecasting**, _Chen et al._, arxiv 2023. \[[paper](https://arxiv.org/abs/2312.16450)\]\[[code](https://github.com/onceCWJ/FCDNet)\]

- **SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion**, _Han et al._, NeurIPS 2024. \[[paper](https://arxiv.org/abs/2404.14197)\]\[[code](https://github.com/Secilia-Cxy/SOFTS)\]

- **SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters**, _Lin et al._, ICML 2024 Oral. \[[paper](https://arxiv.org/abs/2405.00946)\]\[[code](https://github.com/lss-1138/SparseTSF)\]

- **TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting**, _Wang et al._, ICLR 2024. \[[paper](https://arxiv.org/abs/2405.14616)\]\[[code](https://github.com/kwuking/TimeMixer)\]

- **DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting**, _Qiu et al._, KDD 2025. \[[paper](https://arxiv.org/abs/2412.10859)\]\[[code](https://github.com/decisionintelligence/DUET)\]



---



## RNN and CNN



- **DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks**, _Salinas et al._, arxiv 2017. \[[paper](https://arxiv.org/abs/1704.04110)\]\[[TimeSeries](https://github.com/zhykoties/TimeSeries)\]

- **TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis**, _Wu et al._, ICLR 2023. \[[paper](https://arxiv.org/abs/2210.02186)\]\[[code](https://github.com/thuml/TimesNet)\]\[[slides](https://wuhaixu2016.github.io/pdf/ICLR2023_TimesNet.pdf)\]

- **RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks**, _Hou and Yu_, arxiv 2024. \[[paper](https://arxiv.org/abs/2401.09093)\]\[[code](https://github.com/howard-hou/RWKV-TS)\]



---



## Transformer



- **Transformers in Time Series: A Survey**, _Wen et al._, IJCAI 2023. \[[paper](https://arxiv.org/abs/2202.07125)\]\[[code](https://github.com/qingsongedu/time-series-transformers-review)\]

- **Deep Time Series Models: A Comprehensive Survey and Benchmark**, _Wang et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2407.13278)\]\[[code](https://github.com/thuml/Time-Series-Library)\]

- **Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting**, _Zhou et al._, AAAI 2021 Best paper. \[[paper](https://arxiv.org/abs/2012.07436)\]\[[code](https://github.com/zhouhaoyi/Informer2020)\]

- **Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting**, _Wu et al._, NeurIPS 2021. \[[paper](https://arxiv.org/abs/2106.13008)\]\[[code](https://github.com/thuml/Autoformer)\]\[[slides](https://wuhaixu2016.github.io/pdf/NeurIPS2021_Autoformer.pdf)\]\[[ETSformer](https://github.com/salesforce/ETSformer)\]

- **Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy**, _Xu et al._, ICLR 2022. \[[paper](https://arxiv.org/abs/2110.02642)\]\[[code](https://github.com/thuml/Anomaly-Transformer)\]\[[slides](https://wuhaixu2016.github.io/pdf/ICLR2022_Anomaly.pdf)\]\[[TranAD](https://github.com/imperial-qore/TranAD)\]

- **Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting**, _Liu et al._, NeurIPS 2022. \[[paper](https://arxiv.org/abs/2205.14415)\]\[[code](https://github.com/thuml/Nonstationary_Transformers)\]

- **iTransformer: Inverted Transformers Are Effective for Time Series Forecasting**, _Liu et al._, ICLR 2024 Spotlight. \[[paper](https://arxiv.org/abs/2310.06625)\]\[[code](https://github.com/thuml/iTransformer)\]

- **Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting**, _Liu et al._, ICLR 2022. \[[paper](https://openreview.net/forum?id=0EXmFzUn5I)\]\[[code](https://github.com/ant-research/Pyraformer)\]

- **FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting**, _Zhou et al._, ICML 2022. \[[paper](https://arxiv.org/abs/2201.12740)\]\[[code](https://github.com/MAZiqing/FEDformer)\]\[[DAMO-DI-ML](https://github.com/DAMO-DI-ML)\]

- PatchTST: **A Time Series is Worth 64 Words: Long-term Forecasting with Transformers**, _Nie et al._, ICLR 2023. \[[paper](https://arxiv.org/abs/2211.14730)\]\[[code](https://github.com/yuqinie98/PatchTST)\]

- **Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting**, _Zhang and Yan_, ICLR 2023.  \[[paper](https://openreview.net/forum?id=vSVLM2j9eie)\]\[[code](https://github.com/Thinklab-SJTU/Crossformer)\]

- **TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables**, _Wang et al._, NeurIPS 2024. \[[paper](https://arxiv.org/abs/2402.19072)\]\[[code](https://github.com/thuml/TimeXer)\]\[[code](https://github.com/thuml/TimeXer)\]

- **UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting**, _Liu et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2406.04975)\]

- MetaTST: **Metadata Matters for Time Series: Informative Forecasting with Transformers**, _Dong et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2410.03806)\]

- **Are Language Models Actually Useful for Time Series Forecasting**, _Tan et al._, NeurIPS 2024. \[[paper](https://arxiv.org/abs/2406.16964)\]\[[code](https://github.com/BennyTMT/LLMsForTimeSeries)\]\[[CATS](https://github.com/dongbeank/CATS)\]

- **Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective**, _Wang et al._, NeurIPS 2024. \[[paper](https://arxiv.org/abs/2409.18696)\]\[[code](https://github.com/ForestsKing/GLAFF)\]\[[ChatTime](https://github.com/ForestsKing/ChatTime)\]

- **ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer**, _Zhang et al._, NeurIPS 2024. \[[paper](https://arxiv.org/abs/2411.01842)\]\[[code](https://github.com/microsoft/ProbTS/tree/elastst)\]

- **Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting**, _Chen et al._, ICLR 2024. \[[paper](https://arxiv.org/abs/2402.05956)\]\[[code](https://github.com/decisionintelligence/pathformer)\]

- **MASTER: Market-Guided Stock Transformer for Stock Price Forecasting**, _Li et al._, AAAI 2024. \[[paper](https://arxiv.org/abs/2312.15235)\]\[[code](https://github.com/SJTU-DMTai/MASTER)\]\[[StockMixer](https://github.com/SJTU-DMTai/StockMixer)\]\[[Quant-Reading-List](https://github.com/SJTU-DMTai/Quant-Reading-List)\]\[[UMI KDD 2025](https://arxiv.org/abs/2502.04737)\]



---



## GNN



- **A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection**, _Jin et al._, arxiv 2023. \[[paper](https://arxiv.org/abs/2307.03759)\]\[[code](https://github.com/KimMeen/Awesome-GNN4TS)\]

- **GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks**, _Li et al._, NeurIPS 2023. \[[paper](https://arxiv.org/abs/2311.04245)\]\[[code](https://github.com/HKUDS/GPT-ST)\]

- **FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective**, _Yi et al._, NeurIPS 2023. \[[paper](https://arxiv.org/abs/2311.06190)\]\[[code](https://github.com/aikunyi/FourierGNN)\]

- **MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting**, _Cai et al._, AAAI 2024. \[[paper](https://arxiv.org/abs/2401.00423)\]\[[code](https://github.com/YoZhibo/MSGNet)\]



---



## LLM Framework



- **Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook**, _Jin et al._, arxiv 2023. \[[paper](https://arxiv.org/abs/2310.10196)\]\[[code](https://github.com/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM)\]

- **Large Language Models for Time Series: A Survey**, _Zhang et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2402.01801)\]\[[code](https://github.com/xiyuanzh/awesome-llm-time-series)\]\[[Empowering-Time-Series-Analysis-with-LLM](https://github.com/UConn-DSIS/Empowering-Time-Series-Analysis-with-LLM)\]

- **Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review**, _Su et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2402.10350)\]



- **SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling**, _Dong et al._, NeurIPS 2023 Spotlight. \[[paper](https://arxiv.org/abs/2302.00861)\]\[[code](https://github.com/thuml/SimMTM)\]

- **One Fits All: Power General Time Series Analysis by Pretrained LM**, _Zhou et al._, NeurIPS 2023 Spotlight. \[[paper](https://arxiv.org/abs/2302.11939)\]\[[code](https://github.com/DAMO-DI-ML/NeurIPS2023-One-Fits-All)\]\[[AI-for-Time-Series-Papers-Tutorials-Surveys](https://github.com/DAMO-DI-ML/AI-for-Time-Series-Papers-Tutorials-Surveys)\]\[[CALF](https://github.com/Hank0626/CALF)\]

- **Large Language Models Are Zero-Shot Time Series Forecasters**, _Gruver et al._, NeurIPS 2023. \[[paper](https://arxiv.org/abs/2310.07820)\]\[[code](https://github.com/ngruver/llmtime)\]

- **Lag-Llama: Towards Foundation Models for Time Series Forecasting**, _Rasul et al._, arxiv 2023. \[[paper](https://arxiv.org/abs/2310.08278)\]\[[code](https://github.com/time-series-foundation-models/lag-llama)\]

- TimesFM: **A decoder-only foundation model for time-series forecasting**, _Das et al._, ICML 2024. \[[paper](https://arxiv.org/abs/2310.10688)\]\[[code](https://github.com/google-research/timesfm)\]

- **TimeGPT-1**, _Garza et al._, arxiv 2023. \[[paper](https://arxiv.org/abs/2310.03589)\]\[[nixtla](https://github.com/Nixtla/nixtla)\]\[[sulie](https://github.com/wearesulie/sulie)\]

- **Time-LLM: Time Series Forecasting by Reprogramming Large Language Models**, _Jin et al._, ICLR 2024. \[[paper](https://arxiv.org/abs/2310.01728)\]\[[code](https://github.com/KimMeen/Time-LLM)\]

- **AutoTimes: Autoregressive Time Series Forecasters via Large Language Models**, _Liu et al._, NeurIPS 2024. \[[paper](https://arxiv.org/abs/2402.02370)\]\[[code](https://github.com/thuml/AutoTimes)\]

- **Timer: Generative Pre-trained Transformers Are Large Time Series Models**, _Liu et al._, ICML 2024. \[[paper](https://arxiv.org/abs/2402.02368)\]\[[code](https://github.com/thuml/Large-Time-Series-Model)\]\[[Unified Time Series Dataset](https://huggingface.co/datasets/thuml/UTSD)\]\[[website](https://thuml.github.io/timer)\]\[[slides](https://cloud.tsinghua.edu.cn/f/b766629dbc584a4e8563/)\]

- **Timer-XL: Long-Context Transformers for Unified Time Series Forecasting**, _Liu et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2410.04803)\]\[[code](https://github.com/thuml/Timer-XL)\]\[[slides](https://cloud.tsinghua.edu.cn/f/2d4b660fc05148dc8f30/)\]

- **TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling**, _Dong et al._, ICML2024. \[[paper](https://arxiv.org/abs/2402.02475)\]\[[code](https://github.com/thuml/TimeSiam)\]\[[slides](https://cloud.tsinghua.edu.cn/f/99ab4b2aec8b4614b7b4/)\]

- **Sundial: A Family of Highly Capable Time Series Foundation Models**, _Liu et al._, ICML 2025 Oral. \[[paper](https://arxiv.org/abs/2502.00816)\]\[[code](https://github.com/thuml/Sundial)\]

- **MOMENT: A Family of Open Time-series Foundation Models**, _Goswami et al._, ICML 2024. \[[paper](https://arxiv.org/abs/2402.03885)\]\[[code](https://github.com/moment-timeseries-foundation-model/moment)\]

- **Unified Training of Universal Time Series Forecasting Transformers**, _Woo et al._, ICML 2024. \[[paper](https://arxiv.org/abs/2402.02592)\]\[[code](https://github.com/SalesforceAIResearch/uni2ts)\]

- **Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning**, _Bian et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2402.04852)\]

- **UNITS: A Unified Multi-Task Time Series Model**, _Gao et al._, NeurIPS 2024. \[[paper](https://arxiv.org/abs/2403.00131)\]\[[code](https://github.com/mims-harvard/UniTS)\]

- **Chronos: Learning the Language of Time Series**, _Ansari et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2403.07815)\]\[[code](https://github.com/amazon-science/chronos-forecasting)\]

- **ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables**, _Arango et al._, arxiv 2025. \[[paper](https://arxiv.org/abs/2503.12107)\]\[[code](https://github.com/amazon-science/chronos-forecasting/tree/chronosx)\]

- **Large language models can be zero-shot anomaly detectors for time series**, _Alnegheimish et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2405.14755)\]

- **Foundation Models for Time Series Analysis: A Tutorial and Survey**, _Liang et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2403.14735)\]\[[granite-tsfm](https://github.com/ibm-granite/granite-tsfm)\]

- **Are Language Models Actually Useful for Time Series Forecasting?**, _Tan et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2406.16964)\]\[[code](https://github.com/BennyTMT/TS_Models)\]

- **LETS-C: Leveraging Language Embedding for Time Series Classification**, _Kaur et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2407.06533)\]

- **Towards Neural Scaling Laws for Time Series Foundation Models**, _Yao et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2410.12360)\]

- **VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters**, _Chen et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2408.17253)\]\[[code](https://github.com/Keytoyze/VisionTS)\]

- **Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts**, _Shi et al._, ICLR 2025. \[[paper](https://arxiv.org/abs/2409.16040)\]\[[code](https://github.com/Time-MoE/Time-MoE)\]\[[Moirai-MoE](https://arxiv.org/abs/2410.10469)\]

- **ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning**, _Xie et al._, VLDB 2025. \[[paper](https://arxiv.org/abs/2412.03104)\]\[[code](https://github.com/NetManAIOps/ChatTS)\]

- **AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting**, _Benechehab et al._, arxiv 2025. \[[paper](https://arxiv.org/abs/2502.10235)\]\[[code](https://github.com/abenechehab/AdaPTS)\]

- **TimesBERT: A BERT-Style Foundation Model for Time Series Understanding**, _Zhang et al._, arxiv 2025. \[[paper](https://arxiv.org/abs/2502.21245)\]

- **This Time is Different: An Observability Perspective on Time Series Foundation Models**, _Cohen et al._, arxiv 2025. \[[paper](https://arxiv.org/abs/2505.14766)\]\[[code](https://github.com/DataDog/toto)\]

- **Time-R1: Towards Comprehensive Temporal Reasoning in LLMs**, _Liu et al._, arxiv 2025. \[[paper](https://arxiv.org/abs/2505.13508)\]\[[code](https://github.com/ulab-uiuc/Time-R1)\]\[[Position](https://arxiv.org/abs/2502.01477)\]

- **MIRA: Medical Time Series Foundation Model for Real-World Health Data**, _Li et al._, arxiv 2025. \[[paper](https://arxiv.org/abs/2506.07584)\]\[[code](https://github.com/microsoft/MIRA)\]

- **Harnessing Vision-Language Models for Time Series Anomaly Detection**, _He et al._, AAAI 2026 Oral. \[[paper](https://arxiv.org/abs/2506.06836)\]\[[code](https://github.com/ZLHe0/VLM4TS)\]

- **LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence**, _LimiX Team_, arxiv 2025. \[[paper](https://arxiv.org/abs/2509.03505)\]\[[code](https://github.com/limix-ldm-ai/LimiX)\]



---

## Diffusion Model

- **Diffusion-TS: Interpretable Diffusion for General Time Series Generation**, _Yuan and Qiao_, ICLR 2024. \[[paper](https://arxiv.org/abs/2403.01742)\]\[[code](https://github.com/Y-debug-sys/Diffusion-TS)\]

- **A Survey on Diffusion Models for Time Series and Spatio-Temporal Data**, _Yang et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2404.18886)\]\[[code](https://github.com/yyysjz1997/Awesome-TimeSeries-SpatioTemporal-Diffusion-Model)\]

- **TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model**, _Cao et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2409.02322)\]

- **TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation**, _Wang et al._, ICML 2025. \[[paper](https://arxiv.org/abs/2410.05711)\]\[[code](https://github.com/Melmaphother/TimeDART)\]

- **UTSD: Unified Time Series Diffusion Model**, _Ma et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2412.03068)\]

- **Auto-Regressive Moving Diffusion Models for Time Series Forecasting**, _Gao et al._, AAAI 2025. \[[paper](https://arxiv.org/abs/2412.09328)\]\[[code](https://github.com/daxin007/ARMD)\]



---



## Benchmark and Dataset

- **TSPP: A Unified Benchmarking Tool for Time-series Forecasting**, _Bączek et al._, arxiv 2023. \[[paper](https://arxiv.org/abs/2312.17100)\]\[[code](https://github.com/NVIDIA/DeepLearningExamples/tree/master/Tools/PyTorch/TimeSeriesPredictionPlatform)\]

- **TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods**, _Qiu et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2403.20150)\]\[[code](https://github.com/decisionintelligence/TFB)\]

- **A Survey of Generative Techniques for Spatial-Temporal Data Mining**, _Zhang et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2405.09592)\]

- **Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis**, _Liu et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2406.08627)\]\[[code](https://github.com/AdityaLab/Time-MMD)\]\[[MM-TSFlib](https://github.com/AdityaLab/MM-TSFlib)\]

- **GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation**, _Aksu et al._, arxiv 2024. \[[paper](https://arxiv.org/abs/2410.10393)\]\[[code](https://github.com/SalesforceAIResearch/gift-eval)\]

- **FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting**, _Hu et al._, arxiv 2025. \[[paper](https://arxiv.org/abs/2502.18834)\]\[[code](https://github.com/TongjiFinLab/FinTSB)\]

- **It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks**, _Qiao et al._, arxiv 2026. \[[paper](https://arxiv.org/abs/2602.12147)\]

  

- \[[multivariate-time-series-data](https://github.com/laiguokun/multivariate-time-series-data)\]\[[ETDataset](https://github.com/zhouhaoyi/ETDataset)\]\[[Awesome-TimeSeries-SpatioTemporal-Diffusion-Model](https://github.com/yyysjz1997/Awesome-TimeSeries-SpatioTemporal-Diffusion-Model)\]\[[investment_data](https://github.com/chenditc/investment_data)\]



---



## Repositories



- \[[Time-Series-Library](https://github.com/thuml/Time-Series-Library)\]

- \[[time-series-transformers-review](https://github.com/qingsongedu/time-series-transformers-review)\]\[[awesome-AI-for-time-series-papers](https://github.com/qingsongedu/awesome-AI-for-time-series-papers)\]\[[Awesome-TimeSeries-SpatioTemporal-LM-LLM](https://github.com/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM)\]\[[TSFpaper](https://github.com/ddz16/TSFpaper)\]\[[deep-learning-time-series](https://github.com/Alro10/deep-learning-time-series)\]\[[LLMs4TS](https://github.com/wpf535236337/LLMs4TS)\]\[[awesome-time-series-papers](https://github.com/hushuguo/awesome-time-series-papers)\]\[[Awesome Time Series Forecasting Papers and Codes](https://github.com/TongjiFinLab/awesome-time-series-forecasting)\]\[[awesome-time-series-papers](https://github.com/TSCenter/awesome-time-series-papers)\]

- \[[statsforecast](https://github.com/Nixtla/statsforecast)\]\[[neuralforecast](https://github.com/Nixtla/neuralforecast)\]\[[gluonts](https://github.com/awslabs/gluonts)\]\[[Merlion](https://github.com/salesforce/Merlion)\]\[[pytorch-forecasting](https://github.com/jdb78/pytorch-forecasting)\]\[[tsai](https://github.com/timeseriesAI/tsai)\]\[[pytorch-transformer-ts](https://github.com/kashif/pytorch-transformer-ts)\]\[[flow-forecast](https://github.com/AIStream-Peelout/flow-forecast)\]\[[pytorch-ts](https://github.com/zalandoresearch/pytorch-ts)\]

- \[[AIAlpha](https://github.com/VivekPa/AIAlpha)\]

- \[[prophet](https://github.com/facebook/prophet)\]\[[Kats](https://github.com/facebookresearch/Kats)\]\[[tsfresh](https://github.com/blue-yonder/tsfresh)\]\[[sktime](https://github.com/sktime/sktime)\]\[[darts](https://github.com/unit8co/darts)\]\[[tslearn](https://github.com/tslearn-team/tslearn)\]\[[pyflux](https://github.com/RJT1990/pyflux)\]