Awesome PRs Welcome Stars Visits Badge
This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model. This repository is still being continuously improved. If you have found any relevant papers that you would like to be included in this repository, please feel free to submit a pull request (PR) or open an issue.
Each paper may apply to one or several types of forecasting, including univariate time series forecasting, multivariate time series forecasting, and spatio-temporal forecasting, which are also marked in the Type column. If covariates and exogenous variables are not considered, univariate time series forecasting involves predicting the future of one variable with the history of this variable, while multivariate time series forecasting involves predicting the future of C variables with the history of C variables. Note that repeating univariate forecasting multiple times can also achieve the goal of multivariate forecasting. However, univariate forecasting methods cannot extract relationships between variables, so the basis for distinguishing between univariate and multivariate forecasting methods is whether the method involves interaction between variables. Besides, in the era of deep learning, many univariate models can be easily modified to directly process multiple variables for multivariate forecasting. And multivariate models generally can be directly used for univariate forecasting. Here we classify solely based on the model's description in the original paper. Spatio-temporal forecasting is often used in traffic and weather forecasting, and it adds a spatial dimension compared to univariate and multivariate forecasting. In spatio-temporal forecasting, if each measurement point has only one variable, it is equivalent to multivariate forecasting. Therefore, the distinction between spatio-temporal forecasting and multivariate forecasting is not clear. Spatio-temporal models can usually be directly applied to multivariate forecasting, and multivariate models can also be used for spatio-temporal forecasting with minor modifications. Here we also classify solely based on the model's description in the original paper.
- univariate time series forecasting univariate time series forecasting: , where L is the history length, H is the prediction horizon length.
- multivariate time series forecasting multivariate time series forecasting: , where C is the number of variables (channels).
- spatio-temporal forecasting spatio-temporal forecasting: , where N is the spatial dimension (number of measurement points).
| Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|
| 15-11-23 | Multi-step | ACOMP 2015 | Comparison of Strategies for Multi-step-Ahead Prediction of Time Series Using Neural Network | None |
| 19-06-20 | DL | SENSJ 2019 | A Review of Deep Learning Models for Time Series Prediction | None |
| 20-09-27 | DL | Arxiv 2020 | Time Series Forecasting With Deep Learning: A Survey | None |
| 22-02-15 | Transformer | Arxiv 2022 | Transformers in Time Series: A Survey | None |
| 23-03-25 | GNN | Arxiv 2023 | Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey | None |
| 23-05-01 | Diffusion | Arxiv 2023 | Diffusion Models for Time Series Applications: A Survey | None |
| Date | Method | Type | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|---|
| 17-03-21 | LSTNet | multivariate time series forecasting | SIGIR 2018 | Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks | LSTNet |
| 17-04-07 | DA-RNN | univariate time series forecasting | IJCAI 2017 | A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction | DARNN |
| 17-04-13 | DeepAR | univariate time series forecasting | IJoF 2019 | DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks | DeepAR |
| 17-11-29 | MQRNN | univariate time series forecasting | NIPSW 2017 | A Multi-Horizon Quantile Recurrent Forecaster | MQRNN |
| 18-06-23 | mWDN | univariate time series forecasting | KDD 2018 | Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis | mWDN |
| 18-09-06 | MTNet | multivariate time series forecasting | AAAI 2019 | A Memory-Network Based Solution for Multivariate Time-Series Forecasting | MTNet |
| 19-05-28 | DF-Model | multivariate time series forecasting | ICML 2019 | Deep Factors for Forecasting | None |
| 19-07-18 | ESLSTM | univariate time series forecasting | IJoF 2020 | A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting | None |
| 19-07-25 | MH-TAL | univariate time series forecasting | KDD 2019 | Multi-Horizon Time Series Forecasting with Temporal Attention Learning | None |
| 22-05-16 | C2FAR | univariate time series forecasting | NIPS 2022 | C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting | C2FAR |
| 23-06-02 | RNN-ODE-Adap | multivariate time series forecasting | Arxiv 2023 | Neural Differential Recurrent Neural Network with Adaptive Time Steps | RNN_ODE_Adap |
| Date | Method | Type | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|---|
| 19-05-09 | DeepGLO | multivariate time series forecasting | NIPS 2019 | Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting | deepglo |
| 19-05-22 | DSANet | multivariate time series forecasting | CIKM 2019 | DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting | DSANet |
| 19-12-11 | MLCNN | multivariate time series forecasting | AAAI 2020 | Towards Better Forecasting by Fusing Near and Distant Future Visions | MLCNN |
| 21-06-17 | SCINet | multivariate time series forecasting | NIPS 2022 | SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction | SCINet |
| 22-09-22 | MICN | multivariate time series forecasting | ICLR 2023 | MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting | MICN |
| 22-09-22 | TimesNet | multivariate time series forecasting | ICLR 2023 | TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis | TimesNet |
| 23-02-23 | LightCTS | multivariate time series forecasting | SIGMOD 2023 | LightCTS: A Lightweight Framework for Correlated Time Series Forecasting | LightCTS |
| 23-05-25 | TLNets | multivariate time series forecasting | Arxiv 2023 | TLNets: Transformation Learning Networks for long-range time-series prediction | TLNets |
| 23-06-04 | Cross-LKTCN | multivariate time series forecasting | Arxiv 2023 | Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting | None |
| 23-06-12 | MPPN | multivariate time series forecasting | Arxiv 2023 | MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting | None |
| Date | Method | Type | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|---|
| 17-09-14 | STGCN | spatio-temporal forecasting | IJCAI 2018 | Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting | STGCN |
| 19-05-31 | Graph WaveNet | spatio-temporal forecasting | IJCAI 2019 | Graph WaveNet for Deep Spatial-Temporal Graph Modeling | Graph-WaveNet |
| 19-07-17 | ASTGCN | spatio-temporal forecasting | AAAI 2019 | Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting | ASTGCN |
| 20-04-03 | SLCNN | spatio-temporal forecasting | AAAI 2020 | Spatio-Temporal Graph Structure Learning for Traffic Forecasting | None |
| 20-04-03 | GMAN | spatio-temporal forecasting | AAAI 2020 | GMAN: A Graph Multi-Attention Network for Traffic Prediction | GMAN |
| 20-05-03 | MTGNN | multivariate time series forecasting | KDD 2020 | Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks | MTGNN |
| 21-03-13 | StemGNN | multivariate time series forecasting | NIPS 2020 | Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting | StemGNN |
| 22-05-16 | TPGNN | multivariate time series forecasting | NIPS 2022 | Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks | TPGNN |
| 22-06-18 | D2STGNN | spatio-temporal forecasting | VLDB 2022 | Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting | D2STGNN |
| Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|
| 18-05-18 | DSSM | NIPS 2018 | Deep State Space Models for Time Series Forecasting | None |
| 22-08-19 | SSSD | TMLR 2022 | Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models | SSSD |
| 22-09-22 | SpaceTime | ICLR 2023 | Effectively Modeling Time Series with Simple Discrete State Spaces | SpaceTime |
| 22-12-24 | LS4 | ICML 2023 | Deep Latent State Space Models for Time-Series Generation | None |
| Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|
| 20-02-14 | MAF | ICLR 2021 | Multivariate Probabilitic Time Series Forecasting via Conditioned Normalizing Flows | MAF |
| 21-01-18 | TimeGrad | ICML 2021 | Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting | TimeGrad |
| 21-07-07 | CSDI | NIPS 2021 | CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation | CSDI |
| 22-05-16 | MANF | Arxiv 2022 | Multi-scale Attention Flow for Probabilistic Time Series Forecasting | None |
| 22-05-16 | D3VAE | NIPS 2022 | Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement | D3VAE |
| 22-05-16 | LaST | NIPS 2022 | LaST: Learning Latent Seasonal-Trend Representations for Time Series Forecasting | LaST |
| 22-12-28 | Hier-Transformer-CNF | Arxiv 2022 | End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation | None |
| 23-03-13 | HyVAE | Arxiv 2023 | Hybrid Variational Autoencoder for Time Series Forecasting | None |
| 23-06-05 | WIAE | Arxiv 2023 | Non-parametric Probabilistic Time Series Forecasting via Innovations Representation | None |
| 23-06-08 | TimeDiff | ICML 2023 | Non-autoregressive Conditional Diffusion Models for Time Series Prediction | None |
| Date | Method | Type | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|---|
| 17-05-25 | ND | univariate time series forecasting | TNNLS 2017 | Neural Decomposition of Time-Series Data for Effective Generalization | None |
| 17-08-25 | Prophet | univariate time series forecasting | TAS 2018 | Forecasting at Scale | Prophet |
| 22-07-13 | DeepTime | multivariate time series forecasting | ICML 2023 | Learning Deep Time-index Models for Time Series Forecasting | DeepTime |
| 23-06-09 | TimeFlow | univariate time series forecasting | Arxiv 2023 | Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations | None |
| Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|
| 19-02-21 | DAIN | TNNLS 2020 | Deep Adaptive Input Normalization for Time Series Forecasting | DAIN |
| 19-09-19 | DILATE | NIPS 2019 | Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models | DILATE |
| 21-07-19 | TAN | NIPS 2021 | Topological Attention for Time Series Forecasting | TAN |
| 21-09-29 | RevIN | ICLR 2022 | Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift | RevIN |
| 22-02-23 | MQF2 | AISTATS 2022 | Multivariate Quantile Function Forecaster | None |
| 22-05-18 | FiLM | NIPS 2022 | FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting | FiLM |
| 23-02-18 | FrAug | Arxiv 2023 | FrAug: Frequency Domain Augmentation for Time Series Forecasting | FrAug |
| 23-02-22 | Dish-TS | AAAI 2023 | Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting | Dish-TS |
| 23-02-23 | Adaptive Sampling | NIPSW 2022 | Adaptive Sampling for Probabilistic Forecasting under Distribution Shift | None |
| 23-04-19 | RoR | ICML 2023 | Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts | RoR |
| 23-05-28 | PALS | Arxiv 2023 | Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers | None |
| 23-06-09 | FeatureProgramming | ICML 2023 | Feature Programming for Multivariate Time Series Prediction | FeatureProgramming |
| Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|
| 23-02-23 | FPT | Arxiv 2023 | Power Time Series Forecasting by Pretrained LM | FPT |
| Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|
| 22-10-25 | WaveBound | NIPS 2022 | WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting | None |
| 23-05-25 | Ensembling | ICML 2023 | Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting | None |
| Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|
| 16-12-05 | TRMF | NIPS 2016 | Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction | TRMF |