Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/DaoSword/Time-Series-Forecasting-and-Deep-Learning
Resources about time series forecasting and deep learning.
https://github.com/DaoSword/Time-Series-Forecasting-and-Deep-Learning
data-science deep-learning forecasting machine-learning series-data series-forecasting time-series time-series-forecasting
Last synced: 14 days ago
JSON representation
Resources about time series forecasting and deep learning.
- Host: GitHub
- URL: https://github.com/DaoSword/Time-Series-Forecasting-and-Deep-Learning
- Owner: DaoSword
- Created: 2022-07-25T05:32:22.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-29T17:35:20.000Z (14 days ago)
- Last Synced: 2024-10-29T18:57:25.654Z (14 days ago)
- Topics: data-science, deep-learning, forecasting, machine-learning, series-data, series-forecasting, time-series, time-series-forecasting
- Homepage:
- Size: 755 KB
- Stars: 561
- Watchers: 26
- Forks: 54
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Time Series Forecasting and Deep Learning
List of research papers focus on time series forecasting and deep learning, as well as other resources like competitions, datasets, courses, blogs, code, etc.
## Table of Contents
- [Applications](#Applications)
- [Papers](#Papers)
- [2024](#2024)
- [2023](#2023)
- [2022](#2022)
- [2021](#2021)
- [2020](#2020)
- [2019](#2019)
- [2018](#2018)
- [2017](#2017)
- [Blogs](#Blogs)
- [Competitions](#Competitions)
- [Courses](#Courses)
- [Libraries](#Libraries)
- [Datasets](#Datasets)
- [Books](#Books)
- [Repositories](#Repositories)
- [Tutorials](#Tutorials)
## Applications
- [TimeGPT](https://docs.nixtla.io/)
- Nixtla’s `TimeGPT` is a generative pre-trained forecasting model for time series data.
## Papers
### 2024
- [Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting](https://arxiv.org/abs/2401.11929)
- 16 Oct 2024, Jinliang Deng, et al.
- [FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting](https://arxiv.org/abs/2410.11802)
- 15 Oct 2024, Zhe Li, et al.
- [Timer-XL: Long-Context Transformers for Unified Time Series Forecasting](https://arxiv.org/abs/2410.04803)
- 07 Oct 2024, Yong Liu, et al.
- [Autoregressive Moving-average Attention Mechanism for Time Series Forecasting](https://arxiv.org/abs/2410.03159)
- 04 Oct 2024, Jiecheng Lu, et al.
- [[Official Code - ARMA-Attention](https://github.com/ljc-fvnr/arma-attention)]
- [Frequency Adaptive Normalization For Non-stationary Time Series Forecasting](https://arxiv.org/abs/2409.20371)
- 30 Sep 2024, Weiwei Ye, et al.
- [[Official Code - FAN](https://github.com/wayne155/FAN)]
- [Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts](https://arxiv.org/abs/2409.19718)
- 29 Sep 2024, Dalin Qin, et al.
- [[Official Code - EvoMSN](https://github.com/qindalin/evomsn)]
- [CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns](https://arxiv.org/abs/2409.18479)
- 27 Sep 2024, Shengsheng Lin, et al.
- [[Official Code - CycleNet](https://github.com/ACAT-SCUT/CycleNet)]
- [Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecasting](https://arxiv.org/abs/2409.15662)
- 24 Sep 2024, Wenbo Yan, et al.
- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://arxiv.org/abs/2409.16040)
- 24 Sep 2024, Xiaoming Shi, et al.
- [[Official Code - Time-MoE](https://github.com/time-moe/time-moe)]
- [VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters](https://arxiv.org/abs/2408.17253)
- 30 Aug 2024, Mouxiang Chen, et al.
- [[Official Code - VisionTS](https://github.com/keytoyze/visionts)]
- [Mamba or Transformer for Time Series Forecasting? Mixture of Universals (MoU) Is All You Need](https://www.arxiv.org/abs/2408.15997)
- 28 Aug 2024, Sijia Peng, et al.
- [[Official Code - mou](https://github.com/lunaaa95/mou)]
- [PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting](https://arxiv.org/abs/2408.10483)
- 20 Aug 2024, Yongbo Yu, et al.
- [[Official Code - PRformer](https://github.com/usualheart/prformer)]
- [Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators](https://arxiv.org/abs/2401.17548)
- 13 Aug 2024, Lifan Zhao, et al.
- [[Official Code - LIFT](https://github.com/sjtu-dmtai/lift)]
- [Bidirectional Generative Pre-training for Improving Time Series Representation Learning](https://arxiv.org/abs/2402.09558)
- 11 Aug 2024, Ziyang Song, et al.
- [[Official Code - BiTimelyGPT](https://github.com/li-lab-mcgill/bitimelygpt)]
- [Scalable Transformer for High Dimensional Multivariate Time Series Forecasting](https://arxiv.org/abs/2408.04245)
- 08 Aug 2024, Xin Zhou, et al.
- [[Official Code - ScalableTransformer4HighDimensionMTSF](https://github.com/xinzzzhou/scalabletransformer4highdimensionmtsf)]
- [RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms](https://www.arxiv.org/abs/2408.03399)
- 06 Aug 2024, Luis Roque, et al.
- [[Official Code - robustness_hierarchical_time_series_forecasting_algorithms](https://github.com/luisroque/robustness_hierarchical_time_series_forecasting_algorithms)]
- [Fine-grained Attention in Hierarchical Transformers for Tabular Time-series](https://arxiv.org/abs/2406.15327)
- 02 Aug 2024, Raphael Azorin, et al.
- [[Official Code - fieldy](https://github.com/raphaaal/fieldy)]
- [DAM: Towards A Foundation Model for Time Series Forecasting](https://arxiv.org/abs/2407.17880)
- 25 Jul 2024, Luke Darlow, et al.
- [A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting](https://arxiv.org/abs/2407.15909)
- 22 Jul 2024, Pierre-Daniel Arsenault, et al.
- [Deep Time Series Models: A Comprehensive Survey and Benchmark](https://arxiv.org/abs/2407.13278)
- 18 Jul 2024, Yuxuan Wang, et al.
- [[Official Code - Time-Series-Library](https://github.com/thuml/Time-Series-Library)]
- [ViTime: A Visual Intelligence-Based Foundation Model for Time Series Forecasting](https://arxiv.org/abs/2407.07311)
- 10 Jul 2024, Luoxiao Yang, et al.
- [[Official Code - ViTime](https://github.com/IkeYang/ViTime)]
- [S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting](https://arxiv.org/abs/2403.05798)
- 07 Jul 2024, Zijie Pan, et al.
- [[Official Code - S2IP-LLM](https://github.com/panzijie825/s2ip-llm)]
- [Fredformer: Frequency Debiased Transformer for Time Series Forecasting](https://arxiv.org/abs/2406.09009)
- 03 Jul 2024, Xihao Piao, et al.
- [[Official Code - Fredformer](https://github.com/chenzrg/fredformer)]
- [Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling](https://arxiv.org/abs/2402.12694)
- 01 Jul 2024, Guoqi Yu, et al.
- [[Official Code - Leddam](https://github.com/Levi-Ackman/Leddam)]
- [Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization](https://arxiv.org/abs/2407.01622)
- 29 Jun 2024, SheoYon Jhin, et al.
- [[Official Code - CONTIME](https://github.com/sheoyon-jhin/contime)]
- [SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series](https://arxiv.org/abs/2406.17890)
- 25 Jun 2024, Hugo Inzirillo, et al.
- [[Official Code - SigKAN](https://github.com/remigenet/SigKAN)]
- [Are Language Models Actually Useful for Time Series Forecasting?](https://arxiv.org/abs/2406.16964)
- 22 Jun 2024, Mingtian Tan, et al.
- [[Official Code - TS_Models](https://github.com/bennytmt/ts_models)]
- [DeciMamba: Exploring the Length Extrapolation Potential of Mamba](https://arxiv.org/abs/2406.14528)
- 20 Jun 2024, Assaf Ben-Kish, et al.
- [[Official Code - DeciMamba](https://github.com/assafbk/decimamba)]
- [Understanding Different Design Choices in Training Large Time Series Models](https://arxiv.org/abs/2406.14045)
- 20 Jun 2024, Yu-Neng Chuang, et al.
- [[Official Code - ltsm](https://github.com/daochenzha/ltsm/)]
- [Foundation Models for Time Series Analysis: A Tutorial and Survey](https://arxiv.org/abs/2403.14735)
- 18 Jun 2024, Yuxuan Liang, et al.
- [Generative Pretrained Hierarchical Transformer for Time Series Forecasting](https://arxiv.org/abs/2402.16516)
- 18 Jun 2024, Zhiding Liu, et al.
- [[Official Code - GPHT](https://github.com/icantnamemyself/gpht)]
- [ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons](https://arxiv.org/abs/2310.07446)
- 17 Jun 2024, Jiawen Zhang, et al.
- [[Official Code - ProbTS](https://github.com/microsoft/probts)]
- [LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction](https://arxiv.org/abs/2406.10811)
- 16 Jun 2024, Meiyun Wang, et al.
- [SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion](https://arxiv.org/abs/2404.14197)
- 12 Jun 2024, Lu Han, et al.
- [[Official Code - SOFTS](https://github.com/secilia-cxy/softs)]
- [Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis](https://arxiv.org/abs/2406.08627)
- 12 Jun 2024, Haoxin Liu, et al.
- [[Official Code - Time-MMD](https://github.com/adityalab/time-mmd)]
- [A Survey on Diffusion Models for Time Series and Spatio-Temporal Data](https://arxiv.org/abs/2404.18886)
- 11 Jun 2024, Yiyuan Yang, et al.
- [[Official Code - Awesome-TimeSeries-SpatioTemporal-Diffusion-Model](https://github.com/yyysjz1997/awesome-timeseries-spatiotemporal-diffusion-model)]
- [Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift](https://arxiv.org/abs/2310.14838)
- 11 Jun 2024, Mouxiang Chen, et al.
- [[Official Code - Calibration-CDS](https://github.com/half111/calibration_cds)]
- [Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability](https://arxiv.org/abs/2406.02496)
- 04 Jun 2024, Kunpeng Xu, et al.
- [Timer: Generative Pre-trained Transformers Are Large Time Series Models](https://arxiv.org/abs/2402.02368)
- 04 Jun 2024, Yong Liu, et al.
- [[Official Code - Large-Time-Series-Model](https://github.com/thuml/Large-Time-Series-Model)]
- [SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention](https://arxiv.org/abs/2402.10198)
- 03 Jun 2024, Romain Ilbert, et al.
- [[Official Code - samformer](https://github.com/romilbert/samformer)]
- [SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters](https://arxiv.org/abs/2405.00946)
- 03 Jun 2024, Shengsheng Lin, et al.
- [[Official Code - SparseTSF](https://github.com/lss-1138/SparseTSF)]
- [BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition](https://arxiv.org/abs/2308.14906)
- 30 May 2024, Shikai Fang, et al.
- [[Official Code - BayOTIDE](https://github.com/xuangu-fang/bayotide)]
- [Efficient and Effective Time-Series Forecasting with Spiking Neural Networks](https://arxiv.org/abs/2402.01533)
- 29 May 2024, Changze Lv, et al.
- [[Official Code - SeqSNN](https://github.com/microsoft/seqsnn)]
- [UNITS: A Unified Multi-Task Time Series Model](https://arxiv.org/abs/2403.00131)
- 29 May 2024, Shanghua Gao, et al.
- [[Official Code - UniTS](https://github.com/mims-harvard/UniTS)]
- [ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks](https://arxiv.org/abs/2405.18036)
- 28 May 2024, Wanlin Cai, et al.
- [MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting](https://arxiv.org/abs/2405.16440)
- 26 May 2024, Xiuding Cai, et al.
- [[Official Code - MambaTS-pytorch](https://github.com/XiudingCai/MambaTS-pytorch)]
- [CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning](https://arxiv.org/abs/2403.07300)
- 23 May 2024, Peiyuan Liu, et al.
- [[Official Code - CALF](https://github.com/Hank0626/CALF)]
- [TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting](https://arxiv.org/abs/2405.14616)
- 23 May 2024, Shiyu Wang, et al.
- [[Official Code - TimeMixer](https://github.com/kwuking/TimeMixer)]
- [GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing](https://arxiv.org/abs/2405.11333)
- 18 May 2024, Chengqing Yu, et al.
- [[Official Code - GinAR](https://github.com/chengqingyu/ginar)]
- [Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting](https://arxiv.org/abs/2404.15772)
- 17 May 2024, Aobo Liang, et al.
- [DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting](https://arxiv.org/abs/2405.08440)
- 14 May 2024, Qinshuo Liu, et al.
- [Kolmogorov-Arnold Networks (KANs) for Time Series Analysis](https://arxiv.org/abs/2405.08790)
- 14 May 2024, Cristian J. Vaca-Rubio, et al.
- [TKAN: Temporal Kolmogorov-Arnold Networks](https://arxiv.org/abs/2405.07344)
- 12 May 2024, Remi Genet, et al.
- [[Official Code - TKAN](https://github.com/remigenet/tkan)]
- [DTMamba : Dual Twin Mamba for Time Series Forecasting](https://arxiv.org/abs/2405.07022)
- 11 May 2024, Zexue Wu, et al.
- [Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting](https://arxiv.org/abs/2405.06419)
- 10 May 2024, Tianxiang Zhan, et al.
- [[Official Code - TEFN](https://github.com/ztxtech/Time-Evidence-Fusion-Network)]
- [T-Rep: Representation Learning for Time Series using Time-Embeddings](https://arxiv.org/abs/2310.04486)
- 09 May 2024, Archibald Fraikin, et al.
- [[Official Code - T-Rep](https://github.com/let-it-care/t-rep)]
- [A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model](https://arxiv.org/abs/2405.02358)
- 07 May 2024, Jiexia Ye, et al.
- [[Official Code - Awesome-TimeSeries-LLM-FM](https://github.com/start2020/awesome-timeseries-llm-fm)]
- [TSLANet: Rethinking Transformers for Time Series Representation Learning](https://arxiv.org/abs/2404.08472)
- 06 May 2024, Emadeldeen Eldele, et al.
- [[Official Code - TSLANet](https://github.com/emadeldeen24/tslanet)]
- [Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach](https://openreview.net/forum?id=UZlMXUGI6e)
- 02 May 2024, Weijia Zhang, et al.
- [Integrating Mamba and Transformer for Long-Short Range Time Series Forecasting](https://arxiv.org/abs/2404.14757)
- 23 Apr 2024, Xiongxiao Xu, et al.
- [[Official Code - Mambaformer-in-Time-Series](https://github.com/XiongxiaoXu/Mambaformer-in-Time-Series)]
- [Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values](https://openreview.net/forum?id=O9nZCwdGcG)
- 21 Apr 2024, Xiaodan Chen, et al.
- [[Official Code - BiTGraph](https://github.com/chenxiaodanhit/BiTGraph)]
- [A decoder-only foundation model for time-series forecasting](https://arxiv.org/abs/2310.10688)
- 17 Apr 2024, Abhimanyu Das, et al.
- [[Official Code - timesfm](https://github.com/google-research/timesfm)]
- [Towards Transparent Time Series Forecasting](https://openreview.net/forum?id=TYXtXLYHpR)
- 15 Apr 2024, Krzysztof Kacprzyk, et al.
- [Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series](https://arxiv.org/abs/2401.03955)
- 09 Apr 2024, Vijay Ekambaram, et al.
- [[Official Code - granite-tsfm](https://github.com/ibm-granite/granite-tsfm)]
- [ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting](https://arxiv.org/abs/2404.05192)
- 08 Apr 2024, Hengyu Ye, et al.
- [[Official Code - ATFNet](https://github.com/yhyhyhyhyhy/atfnet)]
- [OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting](https://arxiv.org/abs/2304.01506)
- 04 Apr 2023, Xiao He, et al.
- [[Official Code - OneShotSTL](https://github.com/xiao-he/oneshotstl)]
- [Is Mamba Effective for Time Series Forecasting?](https://arxiv.org/abs/2403.11144)
- 02 Apr 2024, Zihan Wang, et al.
- [[Official Code - S-D-Mamba](https://github.com/wzhwzhwzh0921/S-D-Mamba)]
- [TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting](https://arxiv.org/abs/2310.04948)
- 02 Apr 2024, Defu Cao, et al.
- [[Official Code - TEMPO](https://github.com/dc-research/tempo)]
- [From Similarity to Superiority: Channel Clustering for Time Series Forecasting](https://arxiv.org/abs/2404.01340)
- 31 Mar 2024, Jialin Chen, et al.
- [MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection](https://arxiv.org/abs/2403.19888)
- 29 Mar 2024, Ali Behrouz, et al.
- [TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods](https://arxiv.org/abs/2403.20150)
- 29 Mar 2024, Xiangfei Qiu, et al.
- [[Official Code - TFB](https://github.com/decisionintelligence/TFB)]
- [An Analysis of Linear Time Series Forecasting Models](https://arxiv.org/abs/2403.14587)
- 25 Mar 2024, William Toner, et al.
- [[Official Code - linear-forecasting](https://github.com/sir-lab/linear-forecasting)]
- [An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock Forecasting](https://arxiv.org/abs/2404.07969)
- 25 Mar 2024, Chufeng Li, et al.
- [[Official Code - ACEFormer](https://github.com/durandallee/aceformer)]
- [HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/29155)
- 24 Mar 2024, Qihe Huang, et al.
- [Latent Diffusion Transformer for Probabilistic Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/29085)
- 24 Mar 2024, Shibo Feng, et al.
- [StockMixer: A Simple Yet Strong MLP-Based Architecture for Stock Price Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/28681)
- 24 Mar 2024, Jinyong Fan, et al.
- [[Official Code - StockMixer](https://github.com/SJTU-Quant/StockMixer)]
- [ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis](https://openreview.net/forum?id=vpJMJerXHU)
- 22 Mar 2024, Donghao Luo, et al.
- [[Official Code - ModernTCN](https://github.com/luodhhh/ModernTCN)]
- [SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series](https://arxiv.org/abs/2403.15360)
- 22 Mar 2024, Badri N. Patro, et al.
- [[Official Code - simba](https://github.com/badripatro/simba)]
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://arxiv.org/abs/2310.06625)
- 14 Mar 2024, Yong Liu, et al.
- [[Official Code - iTransformer](https://github.com/thuml/iTransformer)]
- [Self-Supervised Learning for Time Series: Contrastive or Generative?](https://arxiv.org/abs/2403.09809)
- 14 Mar 2024, Ziyu Liu, et al.
- [[Official Code - SSL_Comparison](https://github.com/dl4mhealth/ssl_comparison)]
- [TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting](https://arxiv.org/abs/2403.09898)
- 14 Mar 2024, Md Atik Ahamed, et al.
- [[Official Code - TimeMachine](https://github.com/atik-ahamed/timemachine)]
- [TimeDRL: Disentangled Representation Learning for Multivariate Time-Series](https://arxiv.org/abs/2312.04142)
- 13 Mar 2024, Ching Chang, et al.
- [[Official Code - TimeDRL](https://github.com/blacksnail789521/timedrl)]
- [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815)
- 12 Mar 2024, Abdul Fatir Ansari, et al.
- [[Official Code - chronos-forecasting](https://github.com/amazon-science/chronos-forecasting)]
- [Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning](https://arxiv.org/abs/2402.04852)
- 10 Mar 2024, Yuxuan Bian, et al.
- [[Official Code - aLLM4TS](https://github.com/yxbian23/aLLM4TS)]
- [MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process](https://arxiv.org/abs/2403.05751)
- 09 Mar 2024, Xinyao Fan, et al.
- [[Official Code - MG-TSD](https://github.com/hundredl/mg-tsd)]
- [Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting](https://arxiv.org/abs/2403.05406)
- 08 Mar 2024, Muyao Wang, et al.
- [[Official Code - HTV_Trans](https://github.com/flare200020/HTV_Trans)]
- [Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting](https://arxiv.org/abs/2402.05956)
- 07 Mar 2024, Peng Chen, et al.
- [[Official Code - pathformer](https://github.com/decisionintelligence/pathformer)]
- [Periodicity Decoupling Framework for Long-term Series Forecasting](https://openreview.net/forum?id=dp27P5HBBt)
- 06 Mar 2024, Tao Dai, et al.
- [[Official Code - PDF](https://github.com/Hank0626/PDF)]
- [InjectTST: A Transformer Method of Injecting Global Information into Independent Channels for Long Time Series Forecasting](https://arxiv.org/abs/2403.02814)
- 05 Mar 2024, Ce Chi, et al.
- [CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables](https://arxiv.org/abs/2403.01673)
- 04 Mar 2024, Jiecheng Lu, et al.
- [[Official Code - CATS](https://github.com/LJC-FVNR/CATS)]
- [Diffusion-TS: Interpretable Diffusion for General Time Series Generation](https://arxiv.org/abs/2403.01742)
- 04 Mar 2024, Xinyu Yuan, et al.
- [[Official Code - Diffusion-TS](https://github.com/y-debug-sys/diffusion-ts)]
- [Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models](https://arxiv.org/abs/2402.03659)
- 29 Feb 2024, Kelvin Koa, et al.
- [[Official Code - SEP](https://github.com/koa-fin/sep)]
- [TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables](https://arxiv.org/abs/2402.19072)
- 29 Feb 2024, Yuxuan Wang, et al.
- [[Official Code - TimeXer](https://github.com/thuml/timexer)]
- [UniTS: Building a Unified Time Series Model](https://arxiv.org/abs/2403.00131)
- 29 Feb 2024, Shanghua Gao, et al.
- [[Official Code - UniTS](https://github.com/mims-harvard/UniTS)]
- [TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis](https://arxiv.org/abs/2402.16412)
- 26 Feb 2024, Sabera Talukder, et al.
- [[Official Code - TOTEM](https://github.com/saberatalukder/totem)]
- [LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting](https://arxiv.org/abs/2402.16132)
- 25 Feb 2024, Haoxin Liu, et al.
- [[Official Code - lstprompt](https://github.com/AdityaLab/lstprompt)]
- [TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series](https://arxiv.org/abs/2308.08241)
- 22 Feb 2024, Chenxi Sun, et al.
- [[Official Code - TEST](https://github.com/scxsunchenxi/test)]
- [CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting](https://arxiv.org/abs/2305.12095)
- 16 Feb 2024, Wang Xue, et al.
- [[Official Code - CARD](https://github.com/wxie9/card)]
- [ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling](https://arxiv.org/abs/2402.10635)
- 16 Feb 2024, Yuqi Chen, et al.
- [[Official Code - ContiFormer](https://github.com/microsoft/SeqML/tree/main/ContiFormer)]
- [Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review](https://arxiv.org/abs/2402.10350)
- 15 Feb 2024, Jing Su, et al.
- [Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting](https://arxiv.org/abs/2310.08278)
- 08 Feb 2024, Kashif Rasul, et al.
- [[Official Code - lag-llama](https://github.com/time-series-foundation-models/lag-llama)]
- [MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting](https://arxiv.org/abs/2311.18780)
- 08 Feb 2024, Linfeng Du, et al.
- [MOMENT: A Family of Open Time-series Foundation Models](https://arxiv.org/abs/2402.03885)
- 06 Feb 2024, Mononito Goswami, et al.
- [[Official Code - MOMENT](https://anonymous.4open.science/r/BETT-773F/README.md)]
- [DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation](https://arxiv.org/abs/2402.06656)
- 05 Feb 2024, Yuan Gao, et al.
- [Position Paper: What Can Large Language Models Tell Us about Time Series Analysis](https://arxiv.org/abs/2402.02713)
- 05 Feb 2024, Ming Jin, et al.
- [AutoTimes: Autoregressive Time Series Forecasters via Large Language Models](https://arxiv.org/abs/2402.02370)
- 04 Feb 2024, Yong Liu, et al.
- [[Official Code - AutoTimes](https://github.com/thuml/AutoTimes)]
- [FreDF: Learning to Forecast in Frequency Domain](https://arxiv.org/abs/2402.02399)
- 04 Feb 2024, Hao Wang, et al.
- [[Official Code - FreDF](https://github.com/master-plc/fredf)]
- [Unified Training of Universal Time Series Forecasting Transformers](https://arxiv.org/abs/2402.02592)
- 04 Feb 2024, Gerald Woo, et al.
- [[Official Code - uni2ts](https://github.com/SalesforceAIResearch/uni2ts)]
- [Large Language Models for Time Series: A Survey](https://arxiv.org/abs/2402.01801)
- 02 Feb 2024, Xiyuan Zhang, et al.
- [[Official Code - awesome-llm-time-series](https://github.com/xiyuanzh/awesome-llm-time-series)]
- [A Survey of Deep Learning and Foundation Models for Time Series Forecasting](https://arxiv.org/abs/2401.13912)
- 25 Jan 2024, John A. Miller, et al.
- [LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters](https://arxiv.org/abs/2308.08469)
- 18 Jan 2024, Ching Chang, et al.
- [MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting](https://arxiv.org/abs/2401.09261)
- 17 Jan 2024, Zongjiang Shang, et al.
- [RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks](https://arxiv.org/abs/2401.09093)
- 17 Jan 2024, Haowen Hou, et al.
- [[Official Code - RWKV-TS](https://github.com/howard-hou/RWKV-TS)]
- [CNN Kernels Can Be the Best Shapelets](https://openreview.net/forum?id=O8ouVV8PjF)
- 16 Jan 2024, Eric Qu, et al.
- [GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings](https://openreview.net/forum?id=c56TWtYp0W)
- 16 Jan 2024, Jingyun Xiao, et al.
- [[Official Code - GAFormer](https://github.com/nerdslab/GAFormer)]
- [Interpretable Sparse System Identification: Beyond Recent Deep Learning Techniques on Time-Series Prediction](https://openreview.net/forum?id=aFWUY3E7ws)
- 16 Jan 2024, Xiaoyi Liu, et al.
- [Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns](https://openreview.net/forum?id=CdjnzWsQax)
- 16 Jan 2024, Hongbin Huang, et al.
- [Leveraging Generative Models for Unsupervised Alignment of Neural Time Series Data](https://openreview.net/forum?id=9zhHVyLY4K)
- 16 Jan 2024, Ayesha Vermani, et al.
- [Self-Supervised Contrastive Learning for Long-term Forecasting](https://openreview.net/forum?id=nBCuRzjqK7)
- 16 Jan 2024, Junwoo Park, et al.
- [[Official Code - Self-Supervised-Contrastive-Forecsating](https://github.com/junwoopark92/Self-Supervised-Contrastive-Forecsating)]
- [SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series](https://openreview.net/forum?id=s9z0HzWJJp)
- 16 Jan 2024, Junyan Cheng, et al.
- [HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting](https://arxiv.org/abs/2401.05012)
- 10 Jan 2024, Shubao Zhao, et al.
- [Universal Time-Series Representation Learning: A Survey](https://arxiv.org/abs/2401.03717)
- 08 Jan 2024, Patara Trirat, et al.
- [[Official Code - awesome-deep-time-series-representations](https://github.com/itouchz/awesome-deep-time-series-representations)]
- [UnetTSF: A Better Performance Linear Complexity Time Series Prediction Model](https://arxiv.org/abs/2401.03001)
- 05 Jan 2024, Chu Li, et al.
- [[Official Code - UnetTSF](https://github.com/lichuustc/UnetTSF)]
- [U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting](https://arxiv.org/abs/2401.02236)
- 04 Jan 2024, Xiang Ma, et al.
- [[Official Code - U-Mixer](https://github.com/XiangMa-Shaun/U-Mixer)]
### 2023
- [MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting](https://arxiv.org/abs/2401.00423)
- 31 Dec 2023, Wanlin Cai, et al.
- [[Official Code - MSGNet](https://github.com/yozhibo/msgnet)]
- [Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting](https://arxiv.org/abs/2312.16790)
- 28 Dec 2023, Zhihao Yu, et al.
- [[Official Code - HMNet](https://github.com/yzhhoward/hmnet)]
- [TSPP: A Unified Benchmarking Tool for Time-series Forecasting](https://arxiv.org/abs/2312.17100)
- 28 Dec 2023, Jan Bączek, et al.
- [[Official Code - TimeSeriesPredictionPlatform](https://github.com/NVIDIA/DeepLearningExamples/tree/master/Tools/PyTorch/TimeSeriesPredictionPlatform)]
- [Continuous-time Autoencoders for Regular and Irregular Time Series Imputation](https://arxiv.org/abs/2312.16581)
- 27 Dec 2023, Hyowon Wi, et al.
- [Learning to Embed Time Series Patches Independently](https://arxiv.org/abs/2312.16427)
- 27 Dec 2023, Seunghan Lee, et al.
- [[Official Code - pits](https://github.com/seunghan96/pits)]
- [TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning](https://arxiv.org/abs/2312.15709)
- 25 Dec 2023, Jiexi Liu, et al.
- [AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting](https://arxiv.org/abs/2312.13038)
- 20 Dec 2023, Raphael Fischer, et al.
- [[Official Code - xpcr](https://github.com/raphischer/xpcr)]
- [CGS-Mask: Making Time Series Predictions Intuitive for All](https://arxiv.org/abs/2312.09513)
- 15 Dec 2023, Feng Lu, et al.
- [Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting](https://arxiv.org/abs/2312.08763)
- 14 Dec 2023, Yanhong Li, et al.
- [[Official Code - DAN](https://github.com/davidanastasiu/dan)]
- [SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation](https://arxiv.org/abs/2312.05790)
- 10 Dec 2023, Hyun Ryu, et al.
- [[Official Code - simpsi](https://github.com/hyun-ryu/simpsi)]
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752)
- 01 Dec 2023, Albert Gu, et al.
- [[Official Code - mamba](https://github.com/state-spaces/mamba)]
- [Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting](https://arxiv.org/abs/2205.14415)
- 24 Nov 2023, Yong Liu, et al.
- [[Official Code - Nonstationary_Transformers](https://github.com/thuml/Nonstationary_Transformers)]
- [FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective](https://arxiv.org/abs/2311.06190)
- 10 Nov 2023, Kun Yi, et al.
- [[Official Code - FourierGNN](https://github.com/aikunyi/fouriergnn)]
- [Frequency-domain MLPs are More Effective Learners in Time Series Forecasting](https://arxiv.org/abs/2311.06184)
- 10 Nov 2023, Kun Yi, et al.
- [[Official Code - FreTS](https://github.com/aikunyi/frets)]
- [Multi-resolution Time-Series Transformer for Long-term Forecasting](https://arxiv.org/abs/2311.04147)
- 07 Nov 2023, Yitian Zhang, et al.
- [[Official Code - MTST](https://github.com/networkslab/MTST)]
- [PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning](https://arxiv.org/abs/2311.03768)
- 07 Nov 2023, Hao Liu, et al.
- [BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis](https://arxiv.org/abs/2310.20496)
- 31 Oct 2023, Zelin Ni, et al.
- [[Official Code - Basisformer](https://github.com/nzl5116190/basisformer)]
- [ProNet: Progressive Neural Network for Multi-Horizon Time Series Forecasting](https://arxiv.org/abs/2310.19322)
- 30 Oct 2023, Yang Lin
- [Hierarchical Ensemble-Based Feature Selection for Time Series Forecasting](https://arxiv.org/abs/2310.17544)
- 26 Oct 2023, Ayşın Tümay, et al.
- [Attention-Based Ensemble Pooling for Time Series Forecasting](https://arxiv.org/abs/2310.16231)
- 24 Oct 2023, Dhruvit Patel, et al.
- [[Official Code - denpool](https://github.com/awikner/denpool)]
- [Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series](https://arxiv.org/abs/2310.13029)
- 19 Oct 2023, Ioannis Nasios, et al.
- [[Official Code - M5_Uncertainty_3rd_place](https://github.com/IoannisNasios/M5_Uncertainty_3rd_place)]
- [A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis](https://arxiv.org/abs/2310.11959)
- 18 Oct 2023, Shuhan Zhong, et al.
- [[Official Code - MSD-Mixer](https://github.com/zshhans/msd-mixer)]
- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://arxiv.org/abs/2310.10196)
- 16 Oct 2023, Ming Jin, et al.
- [[Official Code - awesome-timeseries-spatiotemporal-lm-llm](https://github.com/qingsongedu/awesome-timeseries-spatiotemporal-lm-llm)]
- [UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting](https://arxiv.org/abs/2310.09751)
- 15 Oct 2023, Xu Liu, et al.
- [[Official Code - UniTime](https://github.com/liuxu77/unitime)]
- [Counterfactual Explanations for Time Series Forecasting](https://arxiv.org/abs/2310.08137)
- 12 Oct 2023, Zhendong Wang, et al.
- [[Official Code - counterfactual-explanations-for-forecasting](https://github.com/zhendong3wang/counterfactual-explanations-for-forecasting)]
- [Lag-Llama: Towards Foundation Models for Time Series Forecasting](https://arxiv.org/abs/2310.08278)
- 12 Oct 2023, Kashif Rasul, et al.
- [[Official Code - lag-llama](https://github.com/kashif/pytorch-transformer-ts/tree/main/lag-llama)]
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://arxiv.org/abs/2310.07820)
- 11 Oct 2023, Nate Gruver, et al.
- [[Official Code - llmtime](https://github.com/ngruver/llmtime)]
- [Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain](https://arxiv.org/abs/2310.05063)
- 08 Oct 2023, Gerald Woo, et al.
- [[Official Code - pretrain-time-series-cloudops](https://github.com/salesforceairesearch/pretrain-time-series-cloudops)]
- [Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs](https://arxiv.org/abs/2310.02619)
- 04 Oct 2023, Ilan Naiman, et al.
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://arxiv.org/abs/2310.01728)
- 03 Oct 2023, Ming Jin, et al.
- [[Official Code - Time-LLM](https://github.com/kimmeen/time-llm)]
- [Modality-aware Transformer for Time series Forecasting](https://arxiv.org/abs/2310.01232)
- 02 Oct 2023, Hajar Emami, et al.
- [PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting](https://arxiv.org/abs/2310.00655)
- 01 Oct 2023, Zeying Gong, et al.
- [[Official Code - PatchMixer](https://github.com/Zeying-Gong/PatchMixer)]
- [Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective](https://openreview.net/forum?id=5BqDSw8r5j)
- 22 Sep 2023, Zhiding Liu, et al.
- [OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling](https://arxiv.org/abs/2309.12659)
- 22 Sep 2023, Yi-Fan Zhang, et al.
- [[Official Code - OneNet](https://github.com/yfzhang114/onenet)]
- [WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting](https://arxiv.org/abs/2309.11319)
- 20 Sep 2023, Peiyuan Liu, et al.
- [[Official Code - WFTNet](https://github.com/Hank0626/WFTNet)]
- [Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data](https://arxiv.org/abs/2309.05305)
- 11 Sep 2023, Yucheng Wang, et al.
- [[Official Code - FCSTGNN](https://github.com/Frank-Wang-oss/FCSTGNN)]
- [PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series](https://arxiv.org/abs/2308.13703)
- 25 Aug 2023, Nicasia Beebe-Wang, et al.
- [[Official Code - irregular timeseries pretraining](https://github.com/google-research/google-research/tree/master/irregular_timeseries_pretraining)]
- [TFDNet: Time-Frequency Enhanced Decomposed Network for Long-term Time Series Forecasting](https://arxiv.org/abs/2308.13386)
- 25 Aug 2023, Yuxiao Luo, et al.
- [[Official Code - TFDNet](https://github.com/YuxiaoLuo0013/TFDNet)]
- [Easy attention: A simple self-attention mechanism for transformer-based time-series reconstruction and prediction](https://arxiv.org/abs/2308.12874)
- 24 Aug 2023, Marcial Sanchis-Agudo, et al.
- [Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting](https://arxiv.org/abs/2308.11946)
- 23 Aug 2023, Yifan Zhang, et al.
- [SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting](https://arxiv.org/abs/2308.11200)
- 22 Aug 2023, Shengsheng Lin, et al.
- [[Official Code - SegRNN](https://github.com/lss-1138/SegRNN)]
- [LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs](https://arxiv.org/abs/2308.08469)
- 16 Aug 2023, Ching Chang, et al.
- [PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer](https://arxiv.org/abs/2308.04791)
- 09 Aug 2023, Shengsheng Lin, et al.
- [DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction](https://arxiv.org/abs/2308.03274)
- 07 Aug 2023, Chengqing Yu, et al.
- [Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3580305.3599378)
- 04 Aug 2023, Arindam Jati, et al.
- [Unsupervised Representation Learning for Time Series: A Review](https://arxiv.org/abs/2308.01578)
- 03 Aug 2023, Qianwen Meng, et al.
- [[Official Code - ULTS](https://github.com/mqwfrog/ULTS)]
- [Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion](https://arxiv.org/abs/2308.01071)
- 02 Aug 2023, Aurélien Renault, et al.
- [[Official Code - Automatic-Feature-Engineering-for-TSC](https://github.com/aurelien-renault/Automatic-Feature-Engineering-for-TSC)]
- [Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach](https://arxiv.org/abs/2308.01011)
- 02 Aug 2023, Chunwei Yang, et al.
- [[Official Code - Floss](https://github.com/agustdd/floss)]
- [SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting](https://dl.acm.org/doi/10.14778/3611540.3611561)
- 01 Aug 2023, Yuanyuan Yao, et al.
- [DeepTSF: Codeless machine learning operations for time series forecasting](https://arxiv.org/abs/2308.00709)
- 28 Jul 2023, Sotiris Pelekis, et al.
- [[Official Code - DeepTSF](https://github.com/I-NERGY/DeepTSF)]
- [TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting](https://arxiv.org/abs/2307.14680)
- 27 Jul 2023, Nancy Xu, et al.
- [[Official Code - Time-GNN](https://github.com/xun468/Time-GNN)]
- [TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers](https://arxiv.org/abs/2307.12667)
- 24 Jul 2023, Md Fahim Sikder, et al.
- [[Official Code - TransFusion](https://github.com/fahim-sikder/TransFusion)]
- [Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting](https://arxiv.org/abs/2307.11494)
- 21 Jul 2023, Marcel Kollovieh, et al.
- [[Official Code - unconditional-time-series-diffusion](https://github.com/amazon-science/unconditional-time-series-diffusion)]
- [TimeTuner: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual Explanations](https://arxiv.org/abs/2307.09916)
- 19 Jul 2023, Jianing Hao, et al.
- [[Official Code - TimeTuner](https://github.com/catherinehao/timetuner)]
- [Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features](https://dl.acm.org/doi/abs/10.1145/3539618.3592013)
- 18 July 2023, Seonmin Kim, et al.
- [[Official Code - Look Ahead](https://github.com/sunsunmin/Look_Ahead)]
- [GBT: Two-stage transformer framework for non-stationary time series forecasting](https://arxiv.org/abs/2307.08302)
- 17 Jul 2023, Li Shen, et al.
- [[Official Code - GBT-Neural_Networks_2023](https://github.com/OrigamiSL/GBT-Neural_Networks_2023)]
- [Sequential Monte Carlo Learning for Time Series Structure Discovery](https://arxiv.org/abs/2307.09607)
- 13 Jul 2023, Feras A. Saad, et al.
- [[Official Code - AutoGP.jl](https://github.com/probsys/AutoGP.jl)]
- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://arxiv.org/abs/2307.03759)
- 07 Jul 2023, Ming Jin, et al.
- [[Official Code - Awesome-GNN4TS](https://github.com/kimmeen/awesome-gnn4ts)]
- [GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting](https://arxiv.org/abs/2307.03595)
- 07 Jul 2023, Sitan Yang, et al.
- [FITS: Modeling Time Series with 10k Parameters](https://arxiv.org/abs/2307.03756)
- 06 Jul 2023, Zhijian Xu, et al.
- [[Official Code - FITS](https://github.com/vewoxic/fits)]
- [SAITS: Self-Attention-based Imputation for Time Series](https://arxiv.org/abs/2202.08516)
- 05 Jul 2023, Wenjie Du, et al.
- [[Official Code - SAITS](https://github.com/WenjieDu/SAITS)]
- [SageFormer: Series-Aware Graph-Enhanced Transformers for Multivariate Time Series Forecasting](https://arxiv.org/abs/2307.01616)
- 04 Jul 2023, Zhenwei Zhang, et al.
- [ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection](https://arxiv.org/abs/2307.00754)
- 03 Jul 2023, Yuhang Chen, et al.
- [[Official Code - IMDiffusion](https://github.com/17000cyh/imdiffusion)]
- [Precursor-of-Anomaly Detection for Irregular Time Series](https://arxiv.org/abs/2306.15489)
- 27 Jun 2023, SheoYon Jhin, et al.
- [[Official Code - PAD](https://github.com/sheoyon-jhin/PAD)]
- [Anomaly Detection with Score Distribution Discrimination](https://arxiv.org/abs/2306.14403)
- 26 Jun 2023, Minqi Jiang, et al.
- [[Official Code - Overlap](https://github.com/Minqi824/Overlap)]
- [InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/25845)
- 26 Jun 2023, Haizhou Cao, et al.
- [Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting](https://arxiv.org/abs/2306.11025)
- 19 Jun 2023, Xinli Yu, et al.
- [DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection](https://arxiv.org/abs/2306.10347)
- 17 Jun 2023, Yiyuan Yang, et al.
- [[Official Code - KDD2023-DCdetector](https://github.com/DAMO-DI-ML/KDD2023-DCdetector)]
- [MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction](https://arxiv.org/abs/2306.10164)
- 16 Jun 2023, Iman Deznabi, et al.
- [[Official Code - MultiWave](https://github.com/information-fusion-lab-umass/multiwave)]
- [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://arxiv.org/abs/2306.10125)
- 16 Jun 2023, Kexin Zhang, et al.
- [[Official Code - Awesome-SSL4TS](https://github.com/qingsongedu/Awesome-SSL4TS)]
- [GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting](https://arxiv.org/abs/2306.08325)
- 14 Jun 2023, YanJun Zhao, et al.
- [[Official Code - GCformer](https://github.com/Yanjun-Zhao/GCformer)]
- [TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting](https://arxiv.org/abs/2306.09364)
- 14 Jun 2023, Vijay Ekambaram, et al.
- [[Official Code - tsfm](https://github.com/ibm/tsfm)]
- [Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping](https://arxiv.org/abs/2306.06994)
- 12 Jun 2023, Luxuan Wang, et al.
- [Feature Programming for Multivariate Time Series Prediction](https://arxiv.org/abs/2306.06252)
- 09 Jun 2023, Alex Reneau, et al.
- [[Official Code - FeatureProgramming](https://github.com/SirAlex900/FeatureProgramming)]
- [Self-Interpretable Time Series Prediction with Counterfactual Explanations](https://arxiv.org/abs/2306.06024)
- 09 Jun 2023, Jingquan Yan, et al.
- [Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations](https://arxiv.org/abs/2306.05880)
- 09 Jun 2023, Etienne Le Naour, et al.
- [Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency](https://arxiv.org/abs/2306.02109)
- 03 Jun 2023, Owen Queen, et al.
- [An End-to-End Time Series Model for Simultaneous Imputation and Forecast](https://arxiv.org/abs/2306.00778)
- 01 Jun 2023, Trang H. Tran, et al.
- [Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context](https://arxiv.org/abs/2306.01112)
- 01 Jun 2023, Oussama Boussif, et al.
- [[Official Code - CrossViVit](https://github.com/gitbooo/CrossViVit)]
- [Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting](https://arxiv.org/abs/2305.18838)
- 30 May 2023, Jiaxin Gao, et al.
- [[Official Code - Client](https://github.com/daxin007/client)]
- [Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors](https://arxiv.org/abs/2305.18803)
- 30 May 2023, Yong Liu, et al.
- [[Official Code - Koopa](https://github.com/thuml/Koopa)]
- [Learning Perturbations to Explain Time Series Predictions](https://arxiv.org/abs/2305.18840)
- 30 May 2023, Joseph Enguehard.
- [[Official Code - time_interpret](https://github.com/josephenguehard/time_interpret)]
- [TLNets: Transformation Learning Networks for long-range time-series prediction](https://arxiv.org/abs/2305.15770)
- 25 May 2023, Wei Wang, et al.
- [[Official Code - TLNets](https://github.com/anonymity111222/tlnets)]
- [A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting](https://arxiv.org/abs/2305.14649)
- 24 May 2023, Yushu Chen, et al.
- [[Official Code - JTFT](https://github.com/rationalspark/jtft)]
- [Forecasting Irregularly Sampled Time Series using Graphs](https://arxiv.org/abs/2305.12932)
- 22 May 2023, Vijaya Krishna Yalavarthi, et al.
- [[Official Code - GraFITi](https://github.com/yalavarthivk/GraFITi)]
- [Learning Structured Components: Towards Modular and Interpretable Multivariate Time Series Forecasting](https://arxiv.org/abs/2305.13036)
- 22 May 2023, Jinliang Deng, et al.
- [[Official Code - SCNN](https://github.com/KDDtest/SCNN)]
- [Make Transformer Great Again for Time Series Forecasting: Channel Aligned Robust Dual Transformer](https://arxiv.org/abs/2305.12095)
- 20 May 2023, Wang Xue, et al.
- [Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping](https://arxiv.org/abs/2305.10721)
- 18 May 2023, Zhe Li, et al.
- [[Official Code - RTSF](https://github.com/plumprc/rtsf)]
- [How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?](https://arxiv.org/abs/2305.06587)
- 11 May 2023, Ming Jin, et al.
- [IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers](https://arxiv.org/abs/2305.06741)
- 11 May 2023, Jingge Xiao, et al.
- [[Official Code - ivpvae](https://github.com/jingge326/ivpvae)]
- [CUTS+: High-dimensional Causal Discovery from Irregular Time-series](https://arxiv.org/abs/2305.05890)
- 10 May 2023, Yuxiao Cheng, et al.
- [[Official Code - UNN](https://github.com/jarrycyx/unn)]
- [Causal Discovery from Subsampled Time Series with Proxy Variables](https://arxiv.org/abs/2305.05276)
- 09 May 2023, Mingzhou Liu, et al.
- [[Official Code - proxy_causal_discovery](https://github.com/lmz123321/proxy_causal_discovery)]
- [Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction](https://arxiv.org/abs/2305.08740)
- 09 May 2023, Sheng Xiang, et al.
- [[Official Code - THGNN](https://github.com/TongjiFinLab/THGNN)]
- [Mlinear: Rethink the Linear Model for Time-series Forecasting](https://arxiv.org/abs/2305.04800)
- 08 May 2023, Wei Li, et al.
- [Diffusion Models for Time Series Applications: A Survey](https://arxiv.org/abs/2305.00624)
- 01 May 2023, Lequan Lin, et al.
- [Context Consistency Regularization for Label Sparsity in Time Series](https://openreview.net/forum?id=EvGOdASdHi)
- 25 Apr 2023, Yooju Shin, et al.
- [[Official Code - CrossMatch](https://github.com/kaist-dmlab/CrossMatch)]
- [Prototype-oriented unsupervised anomaly detection for multivariate time series](https://openreview.net/forum?id=3vO4lS6PuF)
- 25 Apr 2023, Yuxin Li, et al.
- [[Official Code - PUAD](https://github.com/BoChenGroup/PUAD)]
- [Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series](https://openreview.net/forum?id=WhRLdsDTBD)
- 25 Apr 2023, Aniruddh Raghu, et al.
- [[Official Code - SMD-SSL](https://github.com/aniruddhraghu/smd-ssl)]
- [Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022](https://arxiv.org/abs/2305.04811)
- 21 Apr 2023, Cheng Zhang, et al.
- [Long-term Forecasting with TiDE: Time-series Dense Encoder](https://arxiv.org/abs/2304.08424)
- 17 Apr 2023, Abhimanyu Das, et al.
- [[Official Code - google-research - tide](https://github.com/google-research/google-research/tree/master/tide)] [[Unofficial Implementation - TiDE](https://github.com/lich99/TiDE)]
- [Financial Time Series Forecasting using CNN and Transformer](https://arxiv.org/abs/2304.04912)
- 11 Apr 2023, Zhen Zeng, et al.
- [The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting](https://arxiv.org/abs/2304.05206)
- 11 Apr 2023, Lu Han, et al.
- [[Official Code](https://github.com/hanlu-nju/channel_independent_mtsf)]
- [Handling Concept Drift in Global Time Series Forecasting](https://arxiv.org/abs/2304.01512)
- 04 Apr 2023, Ziyi Liu, et al.
- [[Official Code](https://github.com/Neal-Liu-Ziyi/Concept_Drift_Handling)]
- [SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting](https://arxiv.org/abs/2303.18205)
- 31 Mar 2023, Xiaochen Zheng, et al.
- [[Official Code - SimTS_Representation_Learning](https://github.com/xingyu617/SimTS_Representation_Learning)]
- [Towards Diverse and Coherent Augmentation for Time-Series Forecasting](https://arxiv.org/abs/2303.14254)
- 24 Mar 2023, Xiyuan Zhang, et al.
- [UniTS: A Universal Time Series Analysis Framework with Self-supervised Representation Learning](https://arxiv.org/abs/2303.13804)
- 24 Mar 2023, Zhiyu Liang, et al.
- [[Official Code](https://github.com/LceOmlet/UniTS)]
- [Conformal Prediction for Time Series with Modern Hopfield Networks](https://arxiv.org/abs/2303.12783)
- 22 Mar 2023, Andreas Auer, et al.
- [[Official Code - HopCPT](https://github.com/ml-jku/hopcpt)]
- [Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning](https://arxiv.org/abs/2303.11716)
- 21 Mar 2023, Dapeng Li, et al.
- [Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting](https://arxiv.org/abs/2303.11000)
- 20 Mar 2023, Terence L van Zyl.
- [[Official Code](https://github.com/Pieter-Cawood/FFORMA-ESRNN)]
- [Discovering Predictable Latent Factors for Time Series Forecasting](https://arxiv.org/abs/2303.10426)
- 18 Mar 2023, Jingyi Hou, et al.
- [[Official Code - discover_PLF](https://github.com/houjingyi-ustb/discover_plf)]
- [TSMixer: An All-MLP Architecture for Time Series Forecasting](https://arxiv.org/abs/2303.06053)
- 10 Mar 2023, Si-An Chen, et al.
- [[Official Code - tsmixer](https://github.com/google-research/google-research/tree/master/tsmixer)]
- [PHILNet: A novel efficient approach for time series forecasting using deep learning](https://www.sciencedirect.com/science/article/pii/S0020025523003183)
- 08 Mar 2023, M.J. Jiménez-Navarro, et al.
- [[Official Code - PHILNet](https://github.com/manjimnav/PHILNet)]
- [Time Series Forecasting with Transformer Models and Application to Asset Management](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4375798)
- 07 Mar 2023, Edmond Lezmi and Jiali Xu.
- [Temporal Dependencies in Feature Importance for Time Series Predictions](https://arxiv.org/abs/2107.14317)
- 06 Mar 2023, Kin Kwan Leung, et al.
- [[Official Code - WinIT](https://github.com/layer6ai-labs/WinIT)]
- [Your time series is worth a binary image: machine vision assisted deep framework for time series forecasting](https://arxiv.org/abs/2302.14390)
- 28 Feb 2023, Luoxiao Yang, et al.
- [[Official Code - machine-vision-assisted-deep-time-series-analysis-MV-DTSA-](https://github.com/ikeyang/machine-vision-assisted-deep-time-series-analysis-mv-dtsa-)]
- [LightCTS: A Lightweight Framework for Correlated Time Series Forecasting](https://arxiv.org/abs/2302.11974)
- 23 Feb 2023, Zhichen Lai, et al.
- [[Official Code - lightcts](https://github.com/ai4cts/lightcts)]
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://arxiv.org/abs/2302.11939)
- 23 Feb 2023, Tian Zhou, et al.
- [[Official Code - NeurIPS2023-One-Fits-All](https://github.com/DAMO-DI-ML/NeurIPS2023-One-Fits-All)]
- [Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting](https://arxiv.org/abs/2302.14829)
- 22 Feb 2023, Wei Fan, et al.
- [[Official Code](https://github.com/weifantt/dish-ts)]
- [FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification](https://arxiv.org/abs/2302.09818)
- 20 Feb 2023, Mingyue Cheng, et al.
- [[Official Code](https://github.com/Mingyue-Cheng/FormerTime)]
- [FrAug: Frequency Domain Augmentation for Time Series Forecasting](https://arxiv.org/abs/2302.09292)
- 18 Feb 2023, Muxi Chen, et al.
- [Improved Online Conformal Prediction via Strongly Adaptive Online Learning](https://arxiv.org/abs/2302.07869)
- 15 Feb 2023, Aadyot Bhatnagar, et al.
- [[Official Code](https://github.com/salesforce/online_conformal)]
- [SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies](https://arxiv.org/abs/2302.05650)
- 11 Feb 2023, Fan Zhou, et al.
- [MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing](https://arxiv.org/abs/2302.04501)
- 09 Feb 2023, Zhe Li, et al.
- [[Official Code - MTS-Mixers](https://github.com/plumprc/MTS-Mixers)]
- [Domain Adaptation for Time Series Under Feature and Label Shifts](https://arxiv.org/abs/2302.03133)
- 06 Feb 2023, Huan He, et al.
- [[Official Code - Raincoat](https://github.com/mims-harvard/raincoat)]
- [Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting](https://openreview.net/forum?id=vSVLM2j9eie)
- 02 Feb 2023, Yunhao Zhang, Junchi Yan
- [[Official Code - Crossformer](https://github.com/Thinklab-SJTU/Crossformer)]
- [MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting](https://openreview.net/forum?id=zt53IDUR1U)
- 02 Feb 2023, Huiqiang Wang, et al.
- [[Official Code - MICN](https://github.com/wanghq21/MICN)]
- [SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling](https://arxiv.org/abs/2302.00861)
- 02 Feb 2023, Jiaxiang Dong, et al.
- [[Official Code - SimMTM](https://github.com/thuml/simmtm)]
- PrimeNet : Pre-Training for Irregular Multivariate Time Series
- [AAAI 2023](https://aaai.org/Conferences/AAAI-23/), Ranak Roy Chowdhury, et al.
- [[Official Code](https://github.com/ranakroychowdhury/PrimeNet)]
- [Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective](https://arxiv.org/abs/2301.11535)
- 27 Jan 2023, Hui He, et al.
- [Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text](https://arxiv.org/abs/2301.10887)
- 26 Jan 2023, Jinghui Liu, et al.
- [Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series](https://arxiv.org/abs/2301.11308)
- 26 Jan 2023, Abdul Fatir Ansari, et al.
- [[Official Code - NCDSSM](https://github.com/clear-nus/NCDSSM)]
- [Multi-view Kernel PCA for Time series Forecasting](https://arxiv.org/abs/2301.09811)
- 24 Jan 2023, Arun Pandey, et al.
- [Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement](https://arxiv.org/abs/2301.03028)
- 08 Jan 2023, Yan Li, et al.
- [[Official Code](https://github.com/PaddlePaddle/PaddleSpatial/tree/main/research/D3VAE)]
- [Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution](https://arxiv.org/abs/2301.02068)
- 05 Jan 2023, Yan Li, et al.
- [[Official Code](https://github.com/PaddlePaddle/PaddleSpatial/tree/main/research/Conformer)]
- [Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem](https://arxiv.org/abs/2301.01772)
- 04 Jan 2023, Peiwang Tang, et al.
- [Neural SDEs for Conditional Time Series Generation and the Signature-Wasserstein-1 metric](https://arxiv.org/abs/2301.01315)
- 03 Jan 2023, Pere Díaz Lozano, et al.
- [[Official Code](https://github.com/pere98diaz/neural-sdes-for-conditional-time-series-generation-and-the-signature-wasserstein-1-metric)]
### 2022
- [End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation](https://arxiv.org/abs/2212.13706)
- 28 Dec 2022, Shiyu Wang, et al.
- [[Official Code](https://github.com/philipperemy/n-beats)]
- [Dynamic Sparse Network for Time Series Classification: Learning What to "see"](https://arxiv.org/abs/2212.09840)
- 19 Dec 2022, Qiao Xiao, et al.
- [[Official Code](https://github.com/qiaoxiao7282/dsn)]
- [Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting](https://arxiv.org/abs/2212.09030)
- 18 Dec 2022, Slawek Smyl, et al.
- [[Official Code](https://github.com/slaweks17/es-adrnn-with-context)]
- [Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation](https://arxiv.org/abs/2212.08262)
- 16 Dec 2022, Yizhou Dang, et al.
- [[Official Code](https://github.com/kinggugu/ticoserec)]
- [First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting](https://arxiv.org/abs/2212.08151)
- 15 Dec 2022, Xiyuan Zhang, et al.
- [[Code](https://github.com/xiyuanzh/gluonts/tree/tdformer/src/gluonts/nursery/TDformer)]
- [Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series](https://arxiv.org/abs/2212.07771)
- 15 Dec 2022, Nghia Duong-Trung, et al.
- [[Official Code](https://github.com/duongtrung/time-series-temporal-saliency-patterns)]
- Area2Area Forecasting: Looser Constraints, Better Predictions (Manuscript submitted to journal Information Sciences)
- [[Official Code](https://github.com/OrigamiSL/A2A)]
- [Sequential Predictive Conformal Inference for Time Series](https://arxiv.org/abs/2212.03463)
- 07 Dec 2022, Chen Xu, et al.
- [[Official Code - SPCI-code](https://github.com/hamrel-cxu/SPCI-code)]
- [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)
- 06 Dec 2022, Zanwei Zhou, et al.
- [[Official Code](https://github.com/Zanue/MTSF_TVT)]
- [DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting](https://proceedings.mlr.press/v162/lan22a.html)
- 06 Dec 2022, Shiyong Lan, et al.
- [[Official Code](https://github.com/SYLan2019/DSTAGNN)]
- [Learning of Cluster-based Feature Importance for Electronic Health Record Time-series](https://proceedings.mlr.press/v162/aguiar22a.html)
- 06 Dec 2022, Henrique Aguiar, et al.
- [[Official Code](https://github.com/hrna-ox/camelot-icml)]
- [CoTMix: Contrastive Domain Adaptation for Time-Series via Temporal Mixup](https://arxiv.org/abs/2212.01555)
- 03 Dec 2022, Emadeldeen Eldele, et al.
- [[Official Code - CoTMix](https://github.com/emadeldeen24/cotmix)]
- [FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting](https://arxiv.org/abs/2212.01209)
- 02 Dec 2022, Maowei Jiang, et al.
- [[Official Code](https://github.com/zero-coder/fecam)]
- [MHCCL: Masked Hierarchical Cluster-wise Contrastive Learning for Multivariate Time Series](https://arxiv.org/abs/2212.01209)
- 02 Dec 2022, Qianwen Meng, et al.
- [[Official Code](https://github.com/mqwfrog/mhccl)]
- [CRU: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data](https://arxiv.org/abs/2211.16653)
- 30 Nov 2022, Sunghyun Sim, et al.
* [AirFormer: Predicting Nationwide Air Quality in China with Transformers](https://arxiv.org/abs/2211.15979)
* 29 Nov 2022, Yuxuan Liang, et al.
* [[Official Code](https://github.com/yoshall/airformer)]
* [Learning Latent Seasonal-Trend Representations for Time Series Forecasting](https://nips.cc/Conferences/2022/Schedule?showEvent=55179)
* 29 Nov 2022, Zhiyuan Wang, et al.
* [[Official Code](https://github.com/zhycs/LaST)]
* [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/abs/2211.14730)
* 27 Nov 2022, Yuqi Nie, et al.
* [[Official Code](https://github.com/yuqinie98/PatchTST)]
* [A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting](https://arxiv.org/abs/2211.02989)
* 05 Nov 2022, Aryan Jadon, et al.
* [[Official Code](https://github.com/aryan-jadon/regression-loss-functions-in-time-series-forecasting-tensorflow)]
* [Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion](https://arxiv.org/abs/2211.02590)
* 04 Nov 2022, Marin Biloš, et al.
* [Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks](https://openreview.net/forum?id=pMumil2EJh)
* 01 Nov 2022, Yijing Liu, et al.
* [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)
* 01 Nov 2022, Yuzhou Chen, et al.
* [TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting](https://arxiv.org/abs/2210.15050)
* 26 Oct 2022, Hyunwook Lee, et al.
- [WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting](https://arxiv.org/abs/2210.14303)
- 25 Oct 2022, Youngin Cho, et al.
- [[Official Code](https://github.com/choyi0521/WaveBound)]
- [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://arxiv.org/abs/2106.09305)
- 13 Oct 2022, Minhao Liu, et al
- [[Official Code - SCINet](https://github.com/cure-lab/SCINet)]
- [Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts](https://arxiv.org/abs/2210.03675)
- 07 Oct 2022, Rui Wang, et al.
- [[Official Code](https://github.com/google-research/google-research/tree/master/KNF)]
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://arxiv.org/abs/2210.02186)
- 05 Oct 2022, Haixu Wu, et al.
- [[Official Code](https://github.com/thuml/timesnet)]
* [Retrieval Based Time Series Forecasting](https://arxiv.org/abs/2209.13525)
* 27 Sep 2022, Baoyu Jing, et al.
* [FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting](https://openreview.net/forum?id=WXjBX7uz7lO)
* 22 Sep 2022, Li Shen, et al.
* [[Official Code](https://github.com/OrigamiSL/FDNet)]
* [PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting](https://arxiv.org/abs/2210.08964)
* 20 Sep 2022, Hao Xue, et al.
* [[Official Code - PISA](https://github.com/haounsw/pisa)]
* [Out-of-Distribution Representation Learning for Time Series Classification](https://arxiv.org/abs/2209.07027)
* 15 Sep 2022, Wang Lu, et al.
* [[Official Code](https://github.com/microsoft/robustlearn/tree/main/diversify)]
* [Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward](https://www.tandfonline.com/doi/full/10.1080/01605682.2022.2118629)
* 05 Sep 2022, Spyros Makridakis, et al.
* [Expressing Multivariate Time Series as Graphs with Time Series Attention Transformer](https://arxiv.org/abs/2208.09300)
* 19 Aug 2022, William T. Ng, et al.
* [[Official Code](https://github.com/radiantresearch/tsat)]
* [Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting](https://dl.acm.org/doi/10.1145/3534678.3539396)
* 14 Aug 2022, Zezhi Shao, et al.
* [[Official Code]](https://github.com/zezhishao/STEP)
* [Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting](https://arxiv.org/abs/2208.05233)
* 10 Aug 2022, Zezhi Shao, et al.
* [[Official Code](https://github.com/zezhishao/stid)]
* [Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect](https://arxiv.org/abs/2207.10941)
* 22 Jul 2022, Li Shen, et al.
* [[Official Code](https://github.com/OrigamiSL/RTNet2022)]
* [Formal Algorithms for Transformers](https://arxiv.org/abs/2207.09238)
* 19 Jul 2022, Mary Phuong, Marcus Hutter
* [Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms](https://arxiv.org/abs/2207.09572)
* 19 Jul 2022, Linbo Liu, et al.
* [[Official Code - gluonts](https://github.com/awslabs/gluonts)]
* [Generalizable Memory-driven Transformer for Multivariate Long Sequence Time-series Forecasting](https://arxiv.org/abs/2207.07827)
* 16 Jul 2022, Xiaoyun Zhao, et al.
* [[Official Code - CLMFormer](https://github.com/mlii0117/CLMFormer)]
* [Learning Deep Time-index Models for Time Series Forecasting](https://arxiv.org/abs/2207.06046)
* 13 Jul 2022, Gerald Woo, et al.
* [[Official Code - DeepTime](https://github.com/salesforce/deeptime)]
* [Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes](https://arxiv.org/abs/2207.06544)
- 13 Jul 2022, Gregory Benton, et al.
- [[Official Code](https://github.com/g-benton/Volt)]
- [Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures](https://arxiv.org/abs/2207.01186)
- 04 Jul 2022, Tianping Zhang, et al.
- [CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/20320)
- 28 Jun 2022, Hui He, et al.
- [Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting](https://arxiv.org/abs/2206.13816)
- 28 Jun 2022, Junchen Ye, et al
- [[Official Code - ESG](https://github.com/liuzh-19/esg)]
- [Utilizing Expert Features for Contrastive Learning of Time-Series Representations](https://arxiv.org/abs/2206.11517)
- 23 Jun 2022, Manuel Nonnenmacher, et al.
- [[Official Code](https://github.com/boschresearch/expclr)]
- [Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://arxiv.org/abs/2206.08496)
- 17 Jun 2022, Xiang Zhang, et al.
- [[Official Code](https://github.com/mims-harvard/tfc-pretraining)]
- [Closed-Form Diffeomorphic Transformations for Time Series Alignment](https://arxiv.org/abs/2206.08107)
- 16 Jun 2022, Iñigo Martinez, et al.
- [[Official Code](https://github.com/imartinezl/difw)]
- [Contrastive Learning for Unsupervised Domain Adaptation of Time Series](https://arxiv.org/abs/2206.06243)
- 13 Jun 2022, Yilmazcan Ozyurt, et al.
- [[Official Code](https://github.com/oezyurty/cluda)]
- [Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting](https://arxiv.org/abs/2206.04038)
- 08 Jun 2022, Amin Shabani, et al.
- [[Official Code](https://github.com/Scaleformer/Scaleformer)]
- [SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series](https://arxiv.org/abs/2205.15875)
- 31 May 2022, Iris A.M. Huijben, et al.
- [[Official Code - SOM-CPC](https://github.com/iamhuijben/som-cpc)]
- [Are Transformers Effective for Time Series Forecasting?](https://arxiv.org/abs/2205.13504)
- 26 May 2022, Ailing Zeng, et al.
- [[Official Code - LTSF-Linear](https://github.com/cure-lab/LTSF-Linear)]
- [FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting](https://arxiv.org/abs/2205.08897)
- 18 May 2022, Tian Zhou, et al.
- [[Official Code](https://github.com/tianzhou2011/FiLM/)]
- [Efficient Automated Deep Learning for Time Series Forecasting](https://arxiv.org/abs/2205.05511)
- 11 May 2022, Difan Deng, et al.
- [[Official Code](https://github.com/automl/Auto-PyTorch)]
- [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/)]
- 28 Apr 2022, Razvan-Gabriel Cirstea, et al.
- [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)
- 25 Apr 2022, Sheo Yon Jhin, et al.
- [[Official Code](https://github.com/sheoyon-jhin/EXIT)]
- [Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction](https://dl.acm.org/doi/abs/10.1145/3485447.3512056)
- 25 Apr 2022, Min Hou, et al.
- [RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph](https://dl.acm.org/doi/abs/10.1145/3485447.3511974)
- 25 Apr 2022, Ruijie Wang, et al.
- [[Official Code](https://github.com/DiMarzioBian/RETE_TheWebConf)]
- [A data filling methodology for time series based on CNN and (Bi)LSTM neural networks](https://arxiv.org/abs/2204.09994)
- 21 Apr 2022, Kostas Tzoumpas, et al.
- [ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data](https://arxiv.org/abs/2203.08321)
- 15 Mar 2022, Mohamed Ragab, et al.
- [[Official Code](https://github.com/emadeldeen24/AdaTime)]
- [DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting](https://arxiv.org/abs/2203.07681)
- 15 Mar 2022, Wei Fan, et al.
- [[Official Code](https://github.com/weifantt/depts)]
- [Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting](https://arxiv.org/abs/2202.11356)
- 23 Feb 2022, Dazhao Du, et al.
- [[Code](https://github.com/ddz16/Preformer)]
- [Adaptive Conformal Predictions for Time Series](https://arxiv.org/abs/2202.07282)
- 15 Feb 2022, Margaux Zaffran, et al.
- [[Official Code](https://github.com/mzaffran/adaptiveconformalpredictionstimeseries)]
- [ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction](https://dl.acm.org/doi/10.1145/3488560.3498444)
- 15 Feb 2022, Liang Zhao, et al.
- [[Official Code](https://github.com/k51/STGSP)]
- [Transformers in Time Series: A Survey](https://arxiv.org/abs/2202.07125)
- 15 Feb 2022, Qingsong Wen, et al.
- [[Official Code](https://github.com/qingsongedu/time-series-transformers-review)]
- [TACTiS: Transformer-Attentional Copulas for Time Series](https://arxiv.org/abs/2202.03528)
- 7 Feb 2022, Alexandre Drouin, et al.
- [[Official Code - TACTiS](https://github.com/ServiceNow/tactis)]
- [CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting](https://arxiv.org/abs/2202.01575)
- 03 Feb 2022, Gerald Woo, et al.
- [[Official Code - CoST](https://github.com/salesforce/CoST)]
- [ETSformer: Exponential Smoothing Transformers for Time-series Forecasting](https://arxiv.org/abs/2202.01381)
- 03 Feb 2022, Gerald Woo, et al.
- [[Official Code - ETSformer](https://github.com/salesforce/ETSformer)]
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://arxiv.org/abs/2201.12740)
- 30 Jan 2022, Tian Zhou, et al.
- [[Official Code - FEDformer](https://github.com/MAZiqing/FEDformer)]
- [N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting](https://arxiv.org/abs/2201.12886)
- 30 Jan 2022, Cristian Challu, et al.
- [[Official Code - n-hits](https://github.com/cchallu/n-hits)]
- [Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift](https://openreview.net/forum?id=cGDAkQo1C0p)
- 29 Jan 2022, Taesung Kim, et al.
- [[Official Code - RevIN](https://github.com/ts-kim/RevIN)]
- [Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting](https://arxiv.org/abs/2201.04828)
- 13 Jan 2022, Ling Chen, et al.
- [[Official Code - MAGNN](https://github.com/shangzongjiang/magnn)]
### 2021
- [AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version](https://arxiv.org/abs/2112.11174)
- 21 Dec 2021, Xinle Wu, et al.
- [[Official Code - AutoCTS](https://github.com/decisionintelligence/AutoCTS)]
- [A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models](https://arxiv.org/abs/2112.09293)
- 17 Dec 2021, Saroj Gopali, et al.
- [TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs](https://arxiv.org/abs/2112.08025)
- 15 Dec 2021, Yushan Liu, et al.
- [[Official Code - TLogic](https://github.com/liu-yushan/tlogic)]
- [Parameter Efficient Deep Probabilistic Forecasting](https://arxiv.org/abs/2112.02905)
- 14 Dec 2021, Olivier Sprangers, et al.
- [[Official Code - PEDPF](https://github.com/elephaint/pedpf)]
- [NeuralProphet: Explainable Forecasting at Scale](https://arxiv.org/abs/2111.15397)
- 29 Nov 2021, Oskar Triebe, et al.
- [[Official Code - NeuralProphet](https://github.com/ourownstory/neural_prophet)]
- [Modeling Irregular Time Series with Continuous Recurrent Units](https://arxiv.org/abs/2111.11344)
- 22 Nov 2021, Mona Schirmer, et al.
- [[Official Code - Continuous-Recurrent-Units](https://github.com/boschresearch/continuous-recurrent-units)]
- [Transferable Time-Series Forecasting under Causal Conditional Shift](https://arxiv.org/abs/2111.03422)
- 05 Nov 2021, Zijian Li, et al.
- [[Official Code - GCA](https://github.com/dmirlab-group/gca)]
- [Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series](https://arxiv.org/abs/2111.02922)
- 04 Nov 2021, Daniel Kramer, et al.
- [[Official Code - mmPLRNN](https://github.com/durstewitzlab/mmplrnn)]
- [ClaSP - Time Series Segmentation](https://dl.acm.org/doi/abs/10.1145/3459637.3482240)
- 30 Oct 2021, Patrick Schäfer, et al.
- [[Official Code](https://github.com/ermshaua/time-series-segmentation-benchmark)]
- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://arxiv.org/abs/2110.13716)
- 26 Oct 2021, Wentao Xu, et al.
- [[Official Code](https://github.com/wentao-xu/hist)]
- [Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting](https://arxiv.org/abs/2110.08255)
- 13 Oct 2021, Kiran Madhusudhanan, et al.
- [[Official Code](https://github.com/18kiran12/Yformer-Time-Series-Forecasting)]
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://arxiv.org/abs/2110.02642)
- 06 Oct 2021, Jiehui Xu, et al.
- [[Official Code - Anomaly-Transformer](https://github.com/thuml/Anomaly-Transformer)]
- [CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning](https://arxiv.org/abs/2109.14778)
- 30 Sep 2021, Garrett Wilson, et al.
- [[Official Code](https://github.com/floft/calda)]
- [Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting](https://openreview.net/forum?id=0EXmFzUn5I)
- 29 Sep 2021, Shizhan Liu, et al.
- [[Code](https://github.com/alipay/Pyraformer)]
- [Long-Range Transformers for Dynamic Spatiotemporal Forecasting](https://arxiv.org/abs/2109.12218)
- 24 Sep 2021, Jake Grigsby, et al
- [[Official Code - spacetimeformer](https://github.com/qdata/spacetimeformer)]
- [DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications](https://arxiv.org/abs/2109.11495)
- 23 Sep 2021, Dongqi Han, et al.
- [[Official Code](https://github.com/dongtsi/deepaid)]
- [CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting](https://arxiv.org/abs/2109.07438)
- 15 Sep 2021, Harshavardhan Kamarthi, et al.
- [[Official Code](https://github.com/adityalab/camul)]
- [Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation](https://arxiv.org/abs/2109.04871)
- 10 Sep 2021, Ziluo Ding, et al.
- [[Official Code](https://github.com/ruizhao26/ste-flownet)]
- [TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting](https://arxiv.org/abs/2108.12784)
- 29 Aug 2021, Li Shen, Yangzhu Wang
- [[Official Code](https://github.com/OrigamiSL/TCCT2021-Neurocomputing-)]
- [Machine learning in the Chinese stock market](https://www.sciencedirect.com/science/article/pii/S0304405X21003743?via%3Dihu)
- 27 Aug 2021, Markus Leippold, et al.
- [Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization](https://dl.acm.org/doi/10.1145/3447548.3467174)
- 14 Aug 2021, Ahmed Abdulaal, et al.
- [[Official Code](https://github.com/eBay/RANSynCoders)]
- [AdaRNN: Adaptive Learning and Forecasting of Time Series](https://arxiv.org/abs/2108.04443)
- 10 Aug 2021, Yuntao Du, et al.
- [[Official Code](https://github.com/jindongwang/transferlearning)]
- [CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation](https://arxiv.org/abs/2107.03502)
- 07 Jul 2021, Yusuke Tashiro, et al.
- [[Official Code - CSDI](https://github.com/ermongroup/csdi)]
- [Spatiotemporal information conversion machine for time-series prediction](https://arxiv.org/abs/2107.01353)
- 03 Jul 2021, Hao Peng, et al.
- [[Official Code - STICM](https://github.com/mahp-scut/sticm)]
- [Time-Series Representation Learning via Temporal and Contextual Contrasting](https://arxiv.org/abs/2106.14112)
- 26 Jun 2021, Emadeldeen Eldele, et al.
- [[Official Code](https://github.com/emadeldeen24/TS-TCC)]
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008)
- 24 Jun 2021, Haixu Wu, et al.
- [[Code](https://github.com/thuml/Autoformer)]
- [TS2Vec: Towards Universal Representation of Time Series](https://arxiv.org/abs/2106.10466)
- 19 Jun 2021, Zhihan Yue, et al.
- [[Code](https://github.com/yuezhihan/ts2vec)]
- [ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models](https://arxiv.org/abs/2106.10121)
- 18 Jun 2021, Tijin Yan, et al.
- [[Official Code](https://github.com/yantijin/ScoreGradPred)]
- [Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction](https://arxiv.org/abs/2106.09305)
- 17 Jun 2021, Minhao Liu, et al.
- [[Code](https://github.com/cure-lab/SCINet)]
- [Voice2Series: Reprogramming Acoustic Models for Time Series Classification](https://arxiv.org/abs/2106.09296)
- 17 Jun 2021, Chao-Han Huck Yang, et al.
- [[Official Code](https://github.com/huckiyang/Voice2Series-Reprogramming)]
- [Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding](https://arxiv.org/abs/2106.00750)
- 01 Jun 2021, Sana Tonekaboni, et al.
- [[Official Code](https://github.com/sanatonek/TNC_representation_learning)]
- [Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting](https://arxiv.org/abs/2105.04100)
- 10 May 2021, Yuzhou Chen, et al.
- [[Official Code](https://github.com/Z-GCNETs/Z-GCNETs)]
- [Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx](https://arxiv.org/abs/2104.05522)
- 12 Apr 2021, Kin G. Olivares, et al.
- [[Code](https://github.com/cchallu/nbeatsx)]
- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://arxiv.org/abs/2103.12057)
- 22 Mar 2021, Pedro Lara-Benítez, et al.
- [[Official Code - TimeSeriesForecasting-DeepLearning](https://github.com/pedrolarben/TimeSeriesForecasting-DeepLearning)]
- [Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting](https://arxiv.org/abs/2103.07719)
- 13 Mar 2021, Defu Cao, et al.
- [[Official Code](https://github.com/microsoft/StemGNN)]
- [FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection](https://dl.acm.org/doi/10.1145/3437963.3441823)
- 08 Mar 2021, Jia Li, et al.
- [[Official Code](https://github.com/jlidw/FluxEV)]
- [Perceiver: General Perception with Iterative Attention](https://arxiv.org/abs/2103.03206)
- 04 Mar 2021, Andrew Jaegle, et al.
- [[Official Code](https://github.com/deepmind/deepmind-research/tree/master/perceiver)] [[Community Code](https://github.com/lucidrains/perceiver-pytorch)]
- [Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series](https://arxiv.org/abs/2103.02164)
- 03 Mar 2021, Yinjun Wu, et al.
- [[Official Code](https://github.com/thuwuyinjun/DGM2)]
- [Domain Adaptation for Time Series Forecasting via Attention Sharing](https://arxiv.org/abs/2102.06828)
- 13 Feb 2021, Xiaoyong Jin, et al.
- [[Official Code](https://github.com/DMIRLAB-Group/SASA)]
- [Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting](https://arxiv.org/abs/2102.00431)
- 31 Jan 2021, Longyuan Li, et al.
- [[Official Code](https://github.com/longyuanli/VSMHN)]
- [Adjusting for Autocorrelated Errors in Neural Networks for Time Series](https://arxiv.org/abs/2101.12578)
- 28 Jan 2021, Fan-Keng Sun, et al.
- [[Official Code](https://github.com/Daikon-Sun/AdjustAutocorrelation)]
- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://arxiv.org/abs/2101.12072)
- 28 Jan 2021, Kashif Rasul, et al.
- [[Official Code - pytorch-ts](https://github.com/zalandoresearch/pytorch-ts)]
- [Long Horizon Forecasting With Temporal Point Processes](https://arxiv.org/abs/2101.02815)
- 08 Jan 2021, Prathamesh Deshpande, et al.
- [[Official Code](https://github.com/pratham16cse/DualTPP)]
- [Do We Really Need Deep Learning Models for Time Series Forecasting?](https://arxiv.org/abs/2101.02118)
- 06 Jan 2021, Shereen Elsayed, et al.
- [[Code](https://github.com/Daniela-Shereen/GBRT-for-TSF)]
- [Conditional Local Convolution for Spatio-temporal Meteorological Forecasting](https://arxiv.org/abs/2101.01000)
- 04 Jan 2021, Haitao Lin, et al.
- [[Official Code](https://github.com/BIRD-TAO/CLCRN)]
### 2020
* [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436)
* 14 Dec 2020, Haoyi Zhou, et al.
* [[Code](https://github.com/zhouhaoyi/Informer2020)]
* [TimeSHAP: Explaining Recurrent Models through Sequence Perturbations](https://arxiv.org/abs/2012.00073)
* 30 Nov 2020, João Bento, et al.
* [[Official Code](https://github.com/feedzai/timeshap)]
* [Conformal prediction for time series](https://arxiv.org/abs/2010.09107)
* 18 Oct 2020, Chen Xu, et al.
* [[Official Code - EnbPI](https://github.com/hamrel-cxu/EnbPI)]
* [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://arxiv.org/abs/2010.02803)
* 06 Oct 2020, George Zerveas, et al.
* [[Code](https://github.com/gzerveas/mvts_transformer)]
* [Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting](https://arxiv.org/abs/2009.05135)
* 10 Sep 2020, Amirreza Farnoosh, et al.
* [[Official Code](https://github.com/ostadabbas/DSARF)]
* [Deep Learning for Anomaly Detection: A Review](https://arxiv.org/abs/2007.02500)
* 06 Jul 2020, Guansong Pang, et al.
* [On Multivariate Singular Spectrum Analysis and its Variants](https://arxiv.org/abs/2006.13448)
* 24 Jun 2020, Anish Agarwal, et al.
* [[Official Code - mSSA](https://github.com/AbdullahO/mSSA)]
* [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://arxiv.org/abs/2005.11650)
* 24 May 2020, Zonghan Wu, et al.
* [[Official Code - MTGNN](https://github.com/nnzhan/MTGNN)]
* [Time Series Data Augmentation for Deep Learning: A Survey](https://arxiv.org/abs/2002.12478)
* 27 Feb 2020, Qingsong Wen, et al.
* [Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case](https://arxiv.org/abs/2001.08317)
* 23 Jan 2020, Neo Wu, et al.
### 2019
* [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://arxiv.org/abs/1912.09363)
* 19 Dec 2019, Bryan Lim, et al.
* [[Code](https://github.com/mattsherar/Temporal_Fusion_Transform)]
* [Towards Better Forecasting by Fusing Near and Distant Future Visions](https://arxiv.org/abs/1912.05122)
* 11 Dec 2019, Jiezhu Cheng, et al.
* [[Official Code - MLCNN-Multivariate-Time-Series](https://github.com/smallGum/MLCNN-Multivariate-Time-Series)]
* [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://arxiv.org/abs/1911.13288)
* 29 Nov 2019, Omer Berat Sezer, et al.
* [DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting](https://dl.acm.org/doi/10.1145/3357384.3358132)
* 03 Nov 2019, Siteng Huang, et al.
* [[Official Code](https://github.com/bighuang624/DSANet)]
* [Time-Series Aware Precision and Recall for Anomaly Detection: Considering Variety of Detection Result and Addressing Ambiguous Labeling](https://dl.acm.org/doi/abs/10.1145/3357384.3358118)
* 03 Nov 2019, Won-Seok Hwang, et al.
* [[Official Code](https://github.com/saurf4ng/eTaPR)]
* [High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes](https://arxiv.org/abs/1910.03002)
* 07 Oct 2019, David Salinas, et al.
* [[Code](https://github.com/awslabs/gluon-ts/tree/dev/src/gluonts/mx/model/gpvar)]
* [Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models](https://arxiv.org/abs/1909.09020)
* 19 Sep 2019, Vincent Le Guen, et al.
* [[Official Code](https://github.com/vincent-leguen/DILATE)]
* [InceptionTime: Finding AlexNet for Time Series Classification](https://arxiv.org/abs/1909.04939)
* 11 Sep 2019, Hassan Ismail Fawaz, et al.
* [[Official Code](https://github.com/hfawaz/InceptionTime)]
* [Time2Vec: Learning a Vector Representation of Time](https://arxiv.org/abs/1907.05321)
* 11 Jul 2019, Seyed Mehran Kazemi, et al.
* [[Code](https://github.com/ojus1/Time2Vec-PyTorch)]
* [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://arxiv.org/abs/1907.00235)
* 29 Jun 2019, Shiyang Li, et al.
* [[Code](https://github.com/mlpotter/Transformer_Time_Series)] [[Community Code](https://github.com/ghsama/ConvTransformerTimeSeries)]
* [Probabilistic Forecasting with Temporal Convolutional Neural Network](https://arxiv.org/abs/1906.04397)
* 11 Jun 2019, Yitian Chen, et al.
* [[Official Code - deepTCN](https://github.com/oneday88/deepTCN)]
* [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://arxiv.org/abs/1905.10437)
* 24 May 2019, Boris N. Oreshkin, et al.
* [[Code](https://github.com/ElementAI/N-BEATS)]
* [Time-Series Event Prediction with Evolutionary State Graph](https://arxiv.org/abs/1905.05006)
* 10 May 2019, Wenjie Hu, et al.
* [[Official Code](https://github.com/VachelHU/EvoNet)]
* [Deep Adaptive Input Normalization for Time Series Forecasting](https://arxiv.org/abs/1902.07892)
* 21 Feb 2019, Nikolaos Passalis, et al.
* [[Official Code](https://github.com/passalis/dain)]
* [Unsupervised Scalable Representation Learning for Multivariate Time Series](https://arxiv.org/abs/1901.10738)
* 30 Jan 2019, Jean-Yves Franceschi, et al.
* [[Official Code](https://github.com/White-Link/UnsupervisedScalableRepresentationLearningTimeSeries)]
* [Causal Discovery with Attention-Based Convolutional Neural Networks](https://www.mdpi.com/2504-4990/1/1/19)
* 07 Jan 2019, Meike Nauta, et al.
* [[Official Code](https://github.com/M-Nauta/TCDF)]
### 2018
- [RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series](https://arxiv.org/abs/1812.01767)
- 05 Dec 2018, Qingsong Wen, et al.
- [[Code](https://github.com/LeeDoYup/RobustSTL)]
- [Deep learning for time series classification: a review](https://arxiv.org/abs/1809.04356)
- 12 Sep 2018, Hassan Ismail Fawaz, et al.
- [[Official Code](https://github.com/hfawaz/dl-4-tsc)]
- [BRITS: Bidirectional Recurrent Imputation for Time Series](https://arxiv.org/abs/1805.10572)
- 27 May 2018, Wei Cao, et al.
- [[Official Code](https://github.com/caow13/BRITS)]
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://arxiv.org/abs/1803.01271)
- 19 Apr 2018, Shaojie Bai, et al.
- [[Official Code - TCN](https://github.com/locuslab/TCN)]
- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://arxiv.org/abs/1803.06917)
- 19 Mar 2018, Justin Sirignano, et al.
### 2017
- [Graph Attention Networks](https://arxiv.org/abs/1710.10903)
- 30 Oct 2017, Petar Veličković, et al.
- [[Code](https://github.com/PetarV-/GAT)]
- [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
- 12 Jun 2017, Ashish Vaswani, et al.
- [[Code](https://github.com/huggingface/transformers)]
- [Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model](https://arxiv.org/abs/1706.03458)
- 12 Jun 2017, Xingjian Shi, et al.
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://arxiv.org/abs/1704.04110)
- 13 Apr 2017, David Salinas, et al.
- [[Code](https://github.com/jdb78/pytorch-forecasting)]
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://arxiv.org/abs/1703.07015)
- 21 Mar 2017, Guokun Lai, et al.
- [[Official Code - LSTNet](https://github.com/laiguokun/LSTNet)]
## Blogs
- [Kolmogorov-Arnold Networks (KANs) for Time Series Forecasting](https://www.datasciencewithmarco.com/blog/kolmogorov-arnold-networks-kans-for-time-series-forecasting)
- [The Annotated Transformer](http://nlp.seas.harvard.edu/annotated-transformer/)
- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)
- [Time-Series Forecasting: Deep Learning vs Statistics — Who Wins?](https://towardsdatascience.com/time-series-forecasting-deep-learning-vs-statistics-who-wins-c568389d02df)
## Competitions
- [GoDaddy - Microbusiness Density Forecasting | Kaggle](https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/)
- [JPX Tokyo Stock Exchange Prediction | Kaggle](https://www.kaggle.com/competitions/jpx-tokyo-stock-exchange-prediction)
- [M Forecasting Competitions](https://github.com/Mcompetitions)
- [Ubiquant Market Prediction | Kaggle](https://www.kaggle.com/competitions/ubiquant-market-prediction)
## Courses
- [Learn Time Series | Kaggle](https://www.kaggle.com/learn/time-series)
- [CS25: Transformers United](https://web.stanford.edu/class/cs25/)
## Libraries
- [aeon](https://github.com/aeon-toolkit/aeon)
- `aeon` is an open-source toolkit for learning from time series.
- [arch](https://github.com/bashtage/arch)
- Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for financial econometrics, written in Python (with Cython and/or Numba used to improve performance)
- [AutoGP.jl](https://github.com/probsys/AutoGP.jl)
- A Julia package for learning the covariance structure of Gaussian process time series models.
- [AutoTS](https://github.com/winedarksea/AutoTS)
- `AutoTS` is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale.
- [BasicTS](https://github.com/zezhishao/BasicTS/tree/master)
- `BasicTS` (**Basic** **T**ime **S**eries) is a PyTorch-based benchmark and toolbox for **time series forecasting** (TSF).
- [Beibo](https://github.com/ssantoshp/Beibo)
- `Beibo` is a Python library that uses several AI prediction models to predict stocks returns over a defined period of time.
- [Cesium](https://cesium-ml.org/)
- `Cesium` is an end-to-end machine learning platform for time-series, from calculation of features to model-building to predictions.
- [Darts](https://unit8co.github.io/darts/)
- `Darts` is a Python library for easy manipulation and forecasting of time series.
- [DeepOD](https://github.com/xuhongzuo/DeepOD)
- `DeepOD` is an open-source python framework for deep learning-based anomaly detection on multivariate data.
- [Flow Forecast](https://flow-forecast.atlassian.net/wiki/spaces/FF/overview)
- `Flow Forecast` is a deep learning PyTorch library for time series forecasting, classification, and anomaly detection.
- [functime](https://github.com/functime-org/functime)
- `functime` is a powerful Python library for production-ready global forecasting and time-series feature extraction on large panel datasets.
- [GluonTS](https://ts.gluon.ai/stable/)
- `GluonTS` is a Python package for probabilistic time series modeling, focusing on deep learning based models.
- [Greykite](https://linkedin.github.io/greykite/)
- The `Greykite` library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.
- [HyperTS](https://github.com/DataCanvasIO/HyperTS)
- A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.
- [Kats](https://facebookresearch.github.io/Kats/)
- `Kats` is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.
- [Luminaire](https://github.com/zillow/luminaire)
- `Luminaire` is a python package that provides ML-driven solutions for monitoring time series data.
- [MAPIE](https://github.com/scikit-learn-contrib/MAPIE)
- A scikit-learn-compatible module for estimating prediction intervals.
- [Merlion](https://github.com/salesforce/Merlion)
- `Merlion` is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance.
- [MM-TSFlib](https://github.com/AdityaLab/MM-TSFlib)
- `MM-TSFlib` is an open-source library for multimodal time-series forecasting based on [Time-MMD](https://github.com/AdityaLab/Time-MMD/) dataset.
- [NeuralForecast](https://nixtla.github.io/neuralforecast/)
- `NeuralForecast` is a Python library for time series forecasting with deep learning models.
- [NeuralProphet](https://github.com/ourownstory/neural_prophet)
- `NeuralProphet` is an easy to learn framework for interpretable time series forecasting. NeuralProphet is built on PyTorch and combines Neural Network and traditional time-series algorithms, inspired by [Facebook Prophet](https://github.com/facebook/prophet) and [AR-Net](https://github.com/ourownstory/AR-Net).
- [PaddleTS](https://github.com/PaddlePaddle/PaddleTS)
- PaddlePaddle-based Time Series Modeling in Python.
- [Pandas TA](https://github.com/twopirllc/pandas-ta)
- Pandas Technical Analysis (`Pandas TA`) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns.
- [Prophet](https://facebook.github.io/prophet/)
- `Prophet` is a forecasting procedure implemented in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts.
- [Puncc](https://github.com/deel-ai/puncc)
- `Puncc` is a python library for predictive uncertainty quantification using conformal prediction.
- [PyBATS](https://github.com/lavinei/pybats)
- `PyBATS` is a package for Bayesian time series modeling and forecasting.
- [PyDaddy](https://github.com/tee-lab/PyDaddy)
- A Python package to discover stochastic differential equations from time series data.
- [PyDMD: Python Dynamic Mode Decomposition](https://github.com/mathLab/PyDMD)
- `PyDMD` is a Python package that uses Dynamic Mode Decomposition for a data-driven model simplification based on spatiotemporal coherent structures.
- [pyFTS](https://github.com/PYFTS/pyFTS)
- An open source library for Fuzzy Time Series in Python.
- [PyPOTS](https://github.com/WenjieDu/PyPOTS)
- A Python Toolbox for Data Mining on Partially-Observed Time Series.
- [Python Outlier Detection (PyOD)](https://github.com/yzhao062/pyod)
- `PyOD` is a comprehensive and scalable Python library for outlier detection (anomaly detection)
- [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/stable/)
- `PyTorch Forecasting` is a PyTorch-based package for forecasting time series with state-of-the-art network architectures.
- [PyTorchTS](https://github.com/zalandoresearch/pytorch-ts)
- `PyTorchTS` is a [PyTorch](https://github.com/pytorch/pytorch) Probabilistic Time Series forecasting framework which provides state of the art PyTorch time series models by utilizing [GluonTS](https://github.com/awslabs/gluon-ts) as its back-end API and for loading, transforming and back-testing time series data sets.
- [pytrendseries](https://github.com/rafa-rod/pytrendseries)
- `pytrendseries` is a Python library for detection of trends in time series like: stock prices, monthly sales, daily temperature of a city and so on.
- [pyts](https://pyts.readthedocs.io/en/stable/)
- `pyts` is a Python package dedicated to time series classification.
- [Qlib](https://github.com/microsoft/qlib)
- `Qlib` is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.
- [RQAlpha](https://github.com/ricequant/rqalpha)
- A extendable, replaceable Python algorithmic backtest & trading framework supporting multiple securities.
- [Scalecast](https://github.com/mikekeith52/scalecast)
- The pratictioner's forecasting library. Including automated model selection, model optimization, pipelines, visualization, and reporting.
- [sequitur](https://github.com/shobrook/sequitur)
- `sequitur` is a library that lets you create and train an autoencoder for sequential data in just two lines of code.
- [skforecast](https://github.com/JoaquinAmatRodrigo/skforecast/)
- `Skforecast` is a Python library that eases using scikit-learn regressors as single and multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (LightGBM, XGBoost, CatBoost, ...).
- [sktime](https://www.sktime.org/en/stable/)
- `sktime` is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks.
- [StatsForecast](https://github.com/Nixtla/statsforecast)
- `StatsForecast` offers a collection of popular univariate time series forecasting models optimized for high performance and scalability.
- [TFTS](https://github.com/LongxingTan/Time-series-prediction)
- `TFTS` (TensorFlow Time Series) is an easy-to-use python package for time series, supporting the classical and SOTA deep learning methods in TensorFlow or Keras.
- [tft-torch](https://github.com/PlaytikaOSS/tft-torch)
- `tft-torch` is a Python library that implements ["Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting"](https://arxiv.org/abs/1912.09363) using pytorch framework.
- [TimeEval](https://github.com/HPI-Information-Systems/TimeEval)
- `TimeEval` is an evaluation tool for time series anomaly detection algorithms.
- [Time Interpret (tint)](https://github.com/josephenguehard/time_interpret)
- This library expands the [Captum library](https://captum.ai/) with a specific focus on time-series.
- [Time Series Library (TSlib)](https://github.com/thuml/Time-Series-Library)
- `TSlib` is an open-source library for deep learning researchers, especially deep time series analysis.
- [TODS](https://github.com/datamllab/tods)
- `TODS` is a full-stack automated machine learning system for outlier detection on multivariate time-series data.
- [transdim](https://github.com/xinychen/transdim)
- Machine learning for transportation data imputation and prediction.
- [tsai](https://github.com/timeseriesAI/tsai)
- `tsai` is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series tasks like classification, regression, forecasting, imputation...
- [tsam](https://github.com/FZJ-IEK3-VSA/tsam)
- `tsam` is a python package which uses different machine learning algorithms for the aggregation of time series.
- [tsaug](https://github.com/arundo/tsaug)
- `tsaug` is a Python package for time series augmentation.
- [TSDB](https://github.com/WenjieDu/TSDB)
- A Python Toolbox to Ease Loading Open-Source Time-Series Datasets.
- [tsfeatures](https://github.com/Nixtla/tsfeatures)
- Calculates various features from time series data. Python implementation of the R package [*tsfeatures*](https://github.com/robjhyndman/tsfeatures).
- [TSFEL](https://github.com/fraunhoferportugal/tsfel)
- Time Series Feature Extraction Library (TSFEL for short) is a Python package for feature extraction on time series data.
- [tsfresh](https://github.com/blue-yonder/tsfresh)
- `tsfresh` provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis, signal processing, and nonlinear dynamics with a robust feature selection algorithm.
- [tslearn](https://github.com/tslearn-team/tslearn)
- `tslearn` is a Python package that provides machine learning tools for the analysis of time series.
- [tspiral](https://github.com/cerlymarco/tspiral)
- A python package for time series forecasting with scikit-learn estimators.
## Datasets
- [ADRepository: Real-world anomaly detection datasets](https://github.com/GuansongPang/ADRepository-Anomaly-detection-datasets)
- [Electricity Transformer Dataset (ETDataset)](https://github.com/zhouhaoyi/ETDataset)
* [Monash Time Series Forecasting Repository](https://forecastingdata.org/)
* [Open Source Asset Pricing](https://www.openassetpricing.com/)
* [Subseasonal Climate Forecasting - Microsoft Research](https://www.microsoft.com/en-us/research/project/subseasonal-climate-forecasting/)
* [UCR Time Series Classification Archive](https://www.cs.ucr.edu/~eamonn/time_series_data_2018/)
* [UEA & UCR Time Series Classification Repository](https://www.timeseriesclassification.com/)
## Books
* [Forecasting: Principles and Practice (3rd ed)](https://otexts.com/fpp3/)
* *Rob J Hyndman and George Athanasopoulos, 2021*
* This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly.
## Repositories
- [awesome-time-series](https://github.com/cuge1995/awesome-time-series)
- List of state of the art papers, code, and other resources focus on time series forecasting.
- [awesome_time_series_in_python](https://github.com/MaxBenChrist/awesome_time_series_in_python)
- This curated list contains python packages for time series analysis.
- [DL4Stock](https://github.com/jwwthu/DL4Stock)
- This is the repository for the collection of deep learning in stock market prediction.
- [pytorch-transformer-ts](https://github.com/kashif/pytorch-transformer-ts)
- Repository of Transformer based PyTorch Time Series Models.
- [Transformers for Time Series](https://github.com/maxjcohen/transformer)
- Implementation of Transformer model (originally from [Attention is All You Need](https://arxiv.org/abs/1706.03762)) applied to Time Series (Powered by [PyTorch](https://pytorch.org/)).
## Tutorials
* [Deep Learning and Machine Learning for Stock Predictions](https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock)
* This is for learning, studying, researching, and analyzing stock in deep learning (DL) and machine learning (ML).
* [Feature Engineering for Time Series Forecasting](https://github.com/trainindata/feature-engineering-for-time-series-forecasting)
* Create lag, window and seasonal features, perform imputation, variable encoding, extract features from datetime, remove outliers, and more.
* [Stock-Prediction-Models](https://github.com/huseinzol05/Stock-Prediction-Models)
* Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations.
* [time-series-forecasting-with-python](https://github.com/jiwidi/time-series-forecasting-with-python)
* A use-case focused tutorial for time series forecasting with python.