Awesome Time Series

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

Table of Contents

• M4 competition
• Kaggle time series competition
• Papers
• Conferences
• Theory-Resource
• Code Resource
• Datasets

M4-competition

M4

papers

• The M4 Competition: 100,000 time series and 61 forecasting methods
• A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
• Weighted ensemble of statistical models
• FFORMA: Feature-based forecast model averaging

Kaggle-time-series-competition

• Walmart Store Sales Forecasting (2014)
• Walmart Sales in Stormy Weather (2015)
• Rossmann Store Sales (2015)
• Wikipedia Web Traffic Forecasting (2017)
• Corporación Favorita Grocery Sales Forecasting (2018)
• Recruit Restaurant Visitor Forecasting (2018)
• COVID19 Global Forecasting (2020)
• Jane Street Future Market Prediction(2021)

Papers

2025

• LETS Forecast: Learning Embedology for Time Series Forecasting ICML 2025
  • [code]

2024

• TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting Arxiv

  • [code]

• UniTS: Building a Unified Time Series Model Arxiv

  • [code]

• Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting Arxiv

  • [code]

2023

• MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting ICLR 2023 Oral

  • [code]

• Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting ICLR 2023

  • [code]

• Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting ICLR 2023

  • [code]

• SAITS: Self-Attention-based Imputation for Time Series Expert Systems with Applications

  • [code]

• A Time Series is Worth 64 Words: Long-term Forecasting with Transformers ICLR 2023

  • [code]

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]

• 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 review

  • Wen, et al.
  • Code

• Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting ICLR 2022 oral

  • Liu, et al.

2021

• A machine learning approach for forecasting hierarchical time series

  • Mancuso, et al.

• Probabilistic Transformer For Time Series Analysis NeuIPS 2021

  • Tang, et al.

• Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting NeuIPS 2021

  • Wu, et al.

• CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation NeuIPS 2021

  • Yusuke, et al.

• Variational Inference for Continuous-Time Switching Dynamical Systems NeuIPS 2021

  • Lukas, et al.

• MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data NeuIPS 2021

  • Zhu, et al.

• Coresets for Time Series Clustering NeuIPS 2021

  • Zhou, et al.

• Online false discovery rate control for anomaly detection in time series NeuIPS 2021

  • Quentin, et al.

• Adjusting for Autocorrelated Errors in Neural Networks for Time Series NeuIPS 2021

  • Sun, et al.

• Deep Explicit Duration Switching Models for Time Series NeuIPS 2021

  • Zhou, et al.

• Deep Learning for Time Series Forecasting: A Survey survey

  • Torres, et al.

• Whittle Networks: A Deep Likelihood Model for Time Series ICML 2021

  • Yu, et al.
  • Code

• Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting ICML 2021

  • Chen, et al.
  • Code

• Long Horizon Forecasting With Temporal Point Processes WSDM 2021

  • Deshpande, et al.
  • Code

• Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting AAAI 2021 best paper

  • Zhou, et al.
  • Code

• Coupled Layer-wise Graph Convolution for Transportation Demand Prediction AAAI 2021

  • Ye, et al.
  • Code

2020

• Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting AAAI 2020

  • Shi, et al.
  • Code

• Adversarial Sparse Transformer for Time Series Forecasting NeurIPS 2020

  • Wu, et al.
  • Code not yet

• Benchmarking Deep Learning Interpretability in Time Series Predictions NeurIPS 2020

  • Ismail, et al.
  • [Code]

• Deep reconstruction of strange attractors from time series NeurIPS 2020

  • Gilpin, et al.
  • [Code]

• Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline classification

  • Tang, et al.
  • [Code]

• [Active Model Selection for Positive Unlabeled Time Series Classificat