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https://github.com/cuge1995/awesome-time-series

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https://github.com/cuge1995/awesome-time-series

List: awesome-time-series

series-forecasting spatio-temporal time-series time-series-forecasting time-series-prediction

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list of papers, code, and other resources

Awesome Lists containing this project

README 

        
List of state of the art papers, code, and other resources focus on time series forecasting. 



## [Table of Contents]()



* [M4 competition](#M4-competition)

* [Kaggle time series competition](#Kaggle-time-series-competition)

* [Papers](#Papers)

* [Conferences](#Conferences)

* [Theory-Resource](#Theory-Resource)

* [Code Resource](#Code-Resource)

* [Datasets](#Datasets)



## M4-competition

[M4](https://github.com/Mcompetitions/M4-methods)



#### papers



* [The M4 Competition: 100,000 time series and 61 forecasting methods](https://www.sciencedirect.com/science/article/pii/S0169207019301128)

* [A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting](https://www.sciencedirect.com/science/article/pii/S0169207019301153)

* [Weighted ensemble of statistical models](https://www.sciencedirect.com/science/article/pii/S0169207019301190#b5)

* [FFORMA: Feature-based forecast model averaging](https://www.sciencedirect.com/science/article/pii/S0169207019300895)



## Kaggle-time-series-competition



* [Walmart Store Sales Forecasting (2014)](https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting)

* [Walmart Sales in Stormy Weather (2015)](https://www.kaggle.com/c/walmart-recruiting-sales-in-stormy-weather)

* [Rossmann Store Sales (2015)](https://www.kaggle.com/c/rossmann-store-sales)

* [Wikipedia Web Traffic Forecasting (2017)](https://www.kaggle.com/c/web-traffic-time-series-forecasting)

* [Corporación Favorita Grocery Sales Forecasting (2018)](https://www.kaggle.com/c/favorita-grocery-sales-forecasting)

* [Recruit Restaurant Visitor Forecasting (2018)](https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting)

* [COVID19 Global Forecasting (2020)](https://www.kaggle.com/c/covid19-global-forecasting-week-5)

* [Jane Street Future Market Prediction(2021)](https://www.kaggle.com/c/jane-street-market-prediction/)



## Papers



### 2024 



- [TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting](https://arxiv.org/pdf/2403.09898.pdf) `Arxiv`

  - [[code](https://github.com/Atik-Ahamed/TimeMachine)]



- [UniTS: Building a Unified Time Series Model](https://arxiv.org/pdf/2403.00131.pdf) `Arxiv`

  - [[code](https://github.com/mims-harvard/units)]



- [Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting](https://arxiv.org/abs/2405.06419) `Arxiv`

  - [[code](https://github.com/ztxtech/Time-Evidence-Fusion-Network)]



### 2023



- [MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting](https://openreview.net/pdf?id=zt53IDUR1U) `ICLR 2023 Oral`

  - [[code](https://github.com/wanghq21/MICN)]



- [Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting](https://openreview.net/pdf?id=vSVLM2j9eie) `ICLR 2023`

  - [[code](https://github.com/Thinklab-SJTU/Crossformer)]



- [Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting](https://openreview.net/pdf?id=sCrnllCtjoE) `ICLR 2023`

  - [[code](https://https://github.com/BorealisAI/scaleformer)]



- [SAITS: Self-Attention-based Imputation for Time Series](https://arxiv.org/abs/2202.08516) `Expert Systems with Applications`

  - [[code](https://github.com/WenjieDu/SAITS/)]



- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](http://arxiv.org/abs/2211.14730) `ICLR 2023`

  - [[code](https://github.com/yuqinie98/PatchTST)]



### 2022



- [Deep Learning for Time Series Anomaly Detection: A Survey]() `survey`



- [A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting]() `survey`

  - [[code](https://github.com/aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-Tensorflow)]



- [Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting]()  `NeurIPS 2022`



- [Generative Time Series Forecasting with Diffusion, Denoise and Disentanglement]()  `NeurIPS 2022`



- [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction]()  `NeurIPS 2022`



- [Learning Latent Seasonal-Trend Representations for Time Series Forecasting]()  `NeurIPS 2022`



- [GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks]()  `NeurIPS 2022`



- [Causal Disentanglement for Time Series]()  `NeurIPS 2022`



- [Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency]()  `NeurIPS 2022`



- [FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting]()  `NeurIPS 2022`



- [BILCO: An Efficient Algorithm for Joint Alignment of Time Series]()  `NeurIPS 2022`



- [LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data]()  `NeurIPS 2022`



- [Unsupervised Learning of Algebraic Structure from Stationary Time Sequences]()  `NeurIPS 2022`



- [Dynamic Sparse Network for Time Series Classification: Learning What to “See”]()  `NeurIPS 2022`



- [WaveBound: Dynamically Bounding Error for Stable Time Series Forecasting]()  `NeurIPS 2022`



- [Conditional Loss and Deep Euler Scheme for Time Series Generation]()  `AAAI 2022`



- [I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series

Analysis and Embedding]() `AAAI 2022`



- [TS2Vec: Towards Universal Representation of Time Series]() `AAAI 2022`



- [Reinforcement Learning based Dynamic Model Combination for Time Series Forecasting]() `AAAI 2022`



- [CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting]() `AAAI 2022`



- [Transformers in Time Series: A Survey](https://arxiv.org/pdf/2202.07125) `review` 

  - Wen, et al.

  - [Code](https://github.com/qingsongedu/time-series-transformers-review)



- [Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting ](https://arxiv.org/pdf/2202.07125) `ICLR 2022 oral` 

  - Liu, et al.



### 2021



- [A machine learning approach for forecasting hierarchical time series](https://www.sciencedirect.com/science/article/pii/S0957417421005431)

  - Mancuso, et al.



- [Probabilistic Transformer For Time Series Analysis](https://openreview.net/forum?id=HfpNVDg3ExA) `NeuIPS 2021` 

  - Tang, et al.



- [Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting](https://papers.nips.cc/paper/2021/file/bcc0d400288793e8bdcd7c19a8ac0c2b-Paper.pdf) `NeuIPS 2021` 

  - Wu, et al.



- [CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation](https://openreview.net/forum?id=VzuIzbRDrum) `NeuIPS 2021` 

  - Yusuke, et al.



- [Variational Inference for Continuous-Time Switching Dynamical Systems](https://openreview.net/forum?id=ake1XpIrDKN) `NeuIPS 2021` 

  - Lukas, et al.



- [MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data](https://openreview.net/forum?id=VeZQA9KdjMK) `NeuIPS 2021` 

  - Zhu, et al.



- [Coresets for Time Series Clustering](https://openreview.net/forum?id=jar9C-V8GH) `NeuIPS 2021` 

  - Zhou, et al.



- [Online false discovery rate control for anomaly detection in time series](https://openreview.net/forum?id=NvN_B_ZEY5c) `NeuIPS 2021` 

  - Quentin, et al.



- [Adjusting for Autocorrelated Errors in Neural Networks for Time Series](https://openreview.net/forum?id=tJ_CO8orSI) `NeuIPS 2021` 

  - Sun, et al.



- [Deep Explicit Duration Switching Models for Time Series](https://openreview.net/forum?id=jar9C-V8GH) `NeuIPS 2021` 

  - Zhou, et al.



- [Deep Learning for Time Series Forecasting: A Survey](https://www.liebertpub.com/doi/pdfplus/10.1089/big.2020.0159) `survey` 

  -  Torres, et al.



- [Whittle Networks: A Deep Likelihood Model for Time Series](https://www.ml.informatik.tu-darmstadt.de/papers/yu2021icml_wspn.pdf) `ICML 2021` 

  - Yu, et al.

  - [Code](https://github.com/ml-research/WhittleNetworks)



- [Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting](https://arxiv.org/abs/2105.04100) `ICML 2021` 

  - Chen, et al.

  - [Code](https://github.com/Z-GCNETs/Z-GCNETs)



- [Long Horizon Forecasting With Temporal Point Processes](https://arxiv.org/abs/2101.02815) `WSDM 2021` 

  - Deshpande, et al.

  - [Code](https://github.com/pratham16cse/DualTPP)



- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) `AAAI 2021 best paper` 

  - Zhou, et al.

  - [Code](https://github.com/zhouhaoyi/Informer2020) 



- [Coupled Layer-wise Graph Convolution for Transportation Demand Prediction](https://arxiv.org/pdf/2012.08080.pdf) `AAAI 2021` 

  - Ye, et al.

  - [Code](https://github.com/Essaim/CGCDemandPrediction) 



### 2020



- [Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/download/6032/5888) `AAAI 2020` 

  - Shi, et al.

  - [Code](https://github.com/huawei-noah/BHT-ARIMA)



- [Adversarial Sparse Transformer for Time Series Forecasting](https://proceedings.neurips.cc/paper/2020/file/c6b8c8d762da15fa8dbbdfb6baf9e260-Paper.pdf) `NeurIPS 2020` 

  - Wu, et al.

  - Code not yet 



- [Benchmarking Deep Learning Interpretability in Time Series Predictions](https://arxiv.org/pdf/2010.13924) `NeurIPS 2020` 

  - Ismail, et al.

  - [[Code](https://github.com/ayaabdelsalam91/TS-Interpretability-Benchmark)]



- [Deep reconstruction of strange attractors from time series](https://proceedings.neurips.cc/paper/2020/hash/021bbc7ee20b71134d53e20206bd6feb-Abstract.html) `NeurIPS 2020` 

  - Gilpin, et al.

  - [[Code](https://github.com/williamgilpin/fnn)]



- [Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline]( https://arxiv.org/abs/2002.10061) `classification` 

  - Tang, et al.

  - [[Code](https://github.com/Wensi-Tang/OS-CNN/)]



- [Active Model Selection for Positive Unlabeled Time Series Classification](https://www.researchgate.net/profile/Shen_Liang7/publication/341691181_Active_Model_Selection_for_Positive_Unlabeled_Time_Series_Classification/links/5ed4ef09458515294527ad45/Active-Model-Selection-for-Positive-Unlabeled-Time-Series-Classification.pdf)

  - Liang, et al.

  - [[Code](https://github.com/sliang11/Active-Model-Selection-for-PUTSC)]



- [Unsupervised Phase Learning and Extraction from Quasiperiodic Multidimensional Time-series Data](https://authors.elsevier.com/a/1b54P5aecShD%7EW)

  - Prayook, et al.

  - [[Code](https://github.com/koonyook/unsupervised-phase-supplementary)]



- [Connecting the Dots: Multivariate Time Series Forecasting withGraph Neural Networks](https://128.84.21.199/pdf/2005.11650.pdf)

  - Wu, et al.

  - [[Code](https://github.com/nnzhan/MTGNN)]



- [Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 Study](https://arxiv.org/pdf/2005.08067.pdf)

  - Löning, et al.

  - Code not yet



- [RobustTAD: Robust Time Series Anomaly Detection viaDecomposition and Convolutional Neural Networks](https://arxiv.org/pdf/2002.09545v1.pdf)

  - Gao, et al.

  - Code not yet



- [Neural Controlled Differential Equations forIrregular Time Series](https://arxiv.org/pdf/2005.08926.pdf)



  - Patrick Kidger, et al.

  - `University of Oxford`

  - [[Code](https://github.com/patrick-kidger/NeuralCDE)]



- [Time Series Forecasting With Deep Learning: A Survey](https://arxiv.org/pdf/2004.13408.pdf)

  - Lim, et al.

  - Code not yet

  

- [Neural forecasting: Introduction and literature overview](https://arxiv.org/pdf/2004.10240.pdf)

  - Benidis, et al.

  - `Amazon Research`

  - Code not yet.



- [Time Series Data Augmentation for Deep Learning: A Survey](https://arxiv.org/pdf/2002.12478.pdf)

  - Wen, et al.

  - Code not yet



- [Modeling time series when some observations are zero](https://www.researchgate.net/profile/Andrew_Harvey5/publication/335035033_Modeling_time_series_when_some_observations_are_zero/links/5d5ea1d5a6fdcc55e81ff273/Modeling-time-series-when-some-observations-are-zero.pdf)```Journal of Econometrics 2020```

  - Andrew Harveyand Ryoko Ito.

  - Code not yet



- [Meta-learning framework with applications to zero-shot time-series forecasting](https://arxiv.org/pdf/2002.02887.pdf)

  - Oreshkin, et al.

  - Code not yet.



- [Harmonic Recurrent Process for Time Series Forecasting](https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/ecai20hr.pdf)

  - Shao-Qun Zhang and Zhi-Hua Zhou.

  - Code not yet.



- [Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting](https://github.com/huawei-noah/BHT-ARIMA)```AAAI 2020```

  - QIQUAN SHI, et al.

  - Code not yet



- [Learnings from Kaggle's Forecasting Competitions](https://www.researchgate.net/publication/339362837_Learnings_from_Kaggle's_Forecasting_Competitions)



  - Casper Solheim Bojer, et al.

  - Code not yet.

  

- [An Industry Case of Large-Scale Demand Forecasting of Hierarchical Components](https://ieeexplore.ieee.org/abstract/document/8999262)



  - Rodrigo Rivera-Castro, et al.

  - Code not yet.

  

- [Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows](https://arxiv.org/pdf/2002.06103.pdf)



  - Kashif Rasul, et al.

  - Code not yet.

  

- [ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting](https://arxiv.org/pdf/2002.04155.pdf)



  - Joel Janek Dabrowski, et al.

  - Code not yet.

  

- [Anomaly detection for Cybersecurity: time series forecasting and deep learning](https://pdfs.semanticscholar.org/810b/dfa0f63f03473be79556b90dc79a88a1f769.pdf)`Good review about forecasting`



  - Giordano Colò.

  - Code not yet.

  

- [Event-Driven Continuous Time Bayesian Networks](https://krvarshney.github.io/pubs/BhattacharjyaSGMVS_aaai2020.pdf)



  - Debarun Bhattacharjya, et al.

  - `Research AI, IBM`

  - Code not yet.



  



## Conferences



   * [ICLR](https://iclr.cc/)

   * [AAAI](https://www.aaai.org/)

   * [IJCAI](https://www.ijcai.org/)

   * [ISF](https://isf.forecasters.org/)

   * [NeurIPS](https://nips.cc/)

   * [ICML](https://icml.cc/)

   * [M5 Competition](https://mofc.unic.ac.cy/m5-competition/)



## Theory-Resource



- [Time Series Analysis, MIT](https://ocw.mit.edu/courses/economics/14-384-time-series-analysis-fall-2013/)



- [Time Series Forecasting, Udacity](https://www.udacity.com/course/time-series-forecasting--ud980)



- [Practical Time Series Analysis, Cousera](https://www.coursera.org/learn/practical-time-series-analysis)



- [Sequences, Time Series and Prediction](https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction)



- [Intro to Time Series Analysis in R, Cousera](https://www.coursera.org/projects/intro-time-series-analysis-in-r)



- [Anomaly Detection in Time Series Data with Keras, Corsera](https://www.coursera.org/projects/anomaly-detection-time-series-keras)



- [Applying Data Analytics in Finance, Coursera](https://www.coursera.org/learn/applying-data-analytics-business-in-finance)



- [Time Series Forecasting using Python](https://courses.analyticsvidhya.com/courses/creating-time-series-forecast-using-python)



- [STAT 510: Applied Time Series Analysis, PSU](https://online.stat.psu.edu/statprogram/stat510)



- [Policy Analysis Using Interrupted Time Series, edx](https://www.edx.org/course/policy-analysis-using-interrupted-time-series)



- [Time Series Forecasting in Python](https://www.manning.com/books/time-series-forecasting-in-python-book)



- [time-series-transformers-review](https://github.com/qingsongedu/time-series-transformers-review)



## Code-Resource

- [PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series](https://github.com/WenjieDu/PyPOTS)



- [FOST from microsoft](https://github.com/microsoft/FOST)



- [pyWATTS: Python Workflow Automation Tool for Time-Series](https://github.com/KIT-IAI/pyWATTS)



- [Seglearn: A Python Package for Learning Sequences and Time Series](https://dmbee.github.io/seglearn/)



- [tsflex: Flexible Time Series Processing & Feature Extraction](https://github.com/predict-idlab/tsflex)



- [cesium: Open-Source Platform for Time Series Inference](https://github.com/cesium-ml/cesium)



- [PyTorch Forecasting: A Python Package for time series forecasting with PyTorch](https://github.com/jdb78/pytorch-forecasting)



- [A collection of time series prediction methods: rnn, seq2seq, cnn, wavenet, transformer, unet, n-beats, gan, kalman-filter](https://github.com/LongxingTan/Time-series-prediction)



- [Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series](https://github.com/maxjcohen/transformer)



- [Predicting/hypothesizing the findings of the M4 Competition](https://www.sciencedirect.com/science/article/pii/S0169207019301098)



- [PyFlux](https://github.com/RJT1990/pyflux)



- [HyperTS: A Full-Pipeline Automated Time Series Analysis Toolkit](https://github.com/DataCanvasIO/HyperTS)



- [Time Series Forecasting Best Practices & Examples](https://github.com/microsoft/forecasting)



- [List of tools & datasets for anomaly detection on time-series data](https://github.com/rob-med/awesome-TS-anomaly-detection)



- [python packages for time series analysis](https://github.com/MaxBenChrist/awesome_time_series_in_python)



- [A scikit-learn compatible Python toolbox for machine learning with time series](https://github.com/alan-turing-institute/sktime)



- [plotly-resampler: Visualize large time series data with plotly.py](https://github.com/predict-idlab/plotly-resampler)



- [time series visualization tools](https://github.com/facontidavide/PlotJuggler)



- [A statistical library designed to fill the void in Python's time series analysis capabilities](https://github.com/alkaline-ml/pmdarima)



- [RNN based Time-series Anomaly detector model implemented in Pytorch](https://github.com/chickenbestlover/RNN-Time-series-Anomaly-Detection)



- [ARCH models in Python](https://github.com/bashtage/arch)



- [A Python toolkit for rule-based/unsupervised anomaly detection in time series](https://github.com/arundo/adtk)



- [A curated list of awesome time series databases, benchmarks and papers](https://github.com/xephonhq/awesome-time-series-database)



- [Matrix Profile analysis methods in Python for clustering, pattern mining, and anomaly detection](https://github.com/matrix-profile-foundation/matrixprofile) 



- [Flow Forecast: A deep learning framework for time series forecasting, classification and anomaly detection built in PyTorch](https://github.com/AIStream-Peelout/flow-forecast)



## Datasets

- [TSDB: A Python Toolbox to Ease Loading Open-Source Time-Series Datasets (supporting 119 datasets)](https://github.com/WenjieDu/TSDB)



- [SkyCam: A Dataset of Sky Images and their Irradiance values](https://github.com/vglsd/SkyCam)



- [U.S. Air Pollution Data](https://data.world/data-society/us-air-pollution-data)



- [U.S. Chronic Disease Data](https://data.world/data-society/us-chronic-disease-data)



- [Air quality from UCI](http://archive.ics.uci.edu/ml/datasets/Air+Quality)



- [Seattle freeway traffic speed](https://github.com/zhiyongc/Seattle-Loop-Data)



- [Youth Tobacco Survey Data](https://data.world/data-society/youth-tobacco-survey-data)



- [Singapore Population](https://data.world/hxchua/populationsg)



- [Airlines Delay](https://data.world/data-society/airlines-delay)



- [Airplane Crashes](https://data.world/data-society/airplane-crashes)



- [Electricity dataset from UCI](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014)



- [Traffic dataset from UCI](https://archive.ics.uci.edu/ml/datasets/PEMS-SF)



- [City of Baltimore Crime Data](https://data.world/data-society/city-of-baltimore-crime-data)



- [Discover The Menu](https://data.world/data-society/discover-the-menu)



- [Global Climate Change Data](https://data.world/data-society/global-climate-change-data)



- [Global Health Nutrition Data](https://data.world/data-society/global-health-nutrition-data)



- [Beijing PM2.5 Data Set](https://raw.githubusercontent.com/jbrownlee/Datasets/master/pollution.csv)



- [Airline Passengers dataset](https://github.com/jbrownlee/Datasets/blob/master/airline-passengers.csv)



- [Government Finance Statistics](https://data.world/data-society/government-finance-statistics)



- [Historical Public Debt Data](https://data.world/data-society/historical-public-debt-data)



- [Kansas City Crime Data](https://data.world/data-society/kansas-city-crime-data)



- [NYC Crime Data](https://data.world/data-society/nyc-crime-data)



- [Kaggle-Web Traffic Time Series Forecasting](https://www.kaggle.com/c/web-traffic-time-series-forecasting)