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

list of papers, code, and other resources

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

Repository

list of papers, code, and other resources

https://github.com/agarbuno/awesome-time-series/blob/master/

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)
* [Corporacin 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

### 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)
  - Lning, 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 Col.
  - 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)

Owner

GitHub Events