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 need 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, which is called channel-independent. 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:
, where L is the history length, H is the prediction horizon length.
multivariate time series forecasting:
, where C is the number of variables (channels).
spatio-temporal forecasting:
, where N is the spatial dimension (number of measurement points).
irregular time series: observation/sampling times are irregular.
🚩 2023/11/1: I have marked some recommended papers with 🌟 (Just my personal preference 😉).
🚩 2023/11/1: I have added a new category 
🚩 2023/11/1: I also recommend you to check out some other GitHub repositories about awesome time series papers: time-series-transformers-review, awesome-AI-for-time-series-papers, time-series-papers, deep-learning-time-series.
🚩 2023/11/3: There are some popular toolkits or code libraries that integrate many time series models: Time-Series-Library, Prophet, Darts, Kats, tsai, GluonTS, PyTorchForecasting, tslearn, AutoGluon, flow-forecast, PyFlux.
🚩 2023/12/28: Since the topic of LLM(Large Language Model)+TS(Time Series) has been popular recently, I have introduced a category (LLM) to include related papers. This is distinguished from the Pretrain category. Pretrain mainly contains papers which design agent tasks (contrastive or generative) suitable for time series, and only use large-scale time series data for pre-training.
| 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 | IJCAI 2023 | Transformers in Time Series: A Survey | PaperList |
| 23-03-25 | STGNN | 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 |
| 23-06-16 | SSL | Arxiv 2023 | Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects | None |
| 23-06-20 | OpenSTL | NIPS 2023 | OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning | Benchmark |
| 23-07-07 | GNN | Arxiv 2023 | A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection | PaperList |
| 23-10-09 | BasicTS | Arxiv 2023 | Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis | Benchmark |
| 23-10-11 | ProbTS | Arxiv 2023 | ProbTS: A Unified Toolkit to Probe Deep Time-series Forecasting | Toolkit |
| 23-12-28 | TSPP | Arxiv 2023 | TSPP: A Unified Benchmarking Tool for Time-series Forecasting | TSPP |
| 24-01-05 | Diffusion | Arxiv 2024 | The Rise of Diffusion Models in Time-Series Forecasting | None |
| 24-02-15 | LLM | Arxiv 2024 | Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review | None |
| Date | Method | Type | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|---|
| 19-06-29 | LogTrans |  |
NIPS 2019 | Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting | flowforecast |
| 19-12-19 | TFT🌟 | |
IJoF 2021 | Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting | tft |
| 20-01-23 | InfluTrans | |
Arxiv 2020 | Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case | influenza transformer |
| 20-06-05 | AST | |
NIPS 2020 | Adversarial Sparse Transformer for Time Series Forecasting | AST |
| 20-12-14 | Informer🌟 |  |
AAAI 2021 | Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting | Informer |
| 21-05-22 | ProTran | |
NIPS 2021 | Probabilistic Transformer for Time Series Analysis | None |
| 21-06-24 | Autoformer🌟 | |
NIPS 2021 | Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting | Autoformer |
| 21-09-17 | Aliformer | |
Arxiv 2021 | From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba | None |
| 21-10-05 | Pyraformer | |
ICLR 2022 | Pyraformer: Low-complexity Pyramidal Attention for Long-range Time Series Modeling and Forecasting | Pyraformer |
| 22-01-14 | Preformer | |
ICASSP 2023 | Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting | Preformer |
| 22-01-30 | FEDformer🌟 | |
ICML 2022 | FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting | FEDformer |
| 22-02-03 | ETSformer | |
Arxiv 2022 | ETSformer: Exponential Smoothing Transformers for Time-series Forecasting | etsformer |
| 22-02-07 | TACTiS | |
ICML 2022 | TACTiS: Transformer-Attentional Copulas for Time Series | TACTiS |
| 22-04-28 | Triformer | |
IJCAI 2022 | Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting | Triformer |
| 22-05-27 | TDformer | |
NIPSW 2022 | First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting | TDformer |
| 22-05-28 | Non-stationary Transformer | |
NIPS 2022 | Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting | Non-stationary Transformers |
| 22-06-08 | Scaleformer | |
ICLR 2023 | Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting | Scaleformer |
| 22-08-14 | Quatformer | |
KDD 2022 | Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting | Quatformer |
| 22-08-30 | Persistence Initialization | |
Arxiv 2022 | Persistence Initialization: A novel adaptation of the Transformer architecture for Time Series Forecasting | None |
| 22-09-08 | W-Transformers | |
Arxiv 2022 | W-Transformers: A Wavelet-based Transformer Framework for Univariate Time Series Forecasting | w-transformer |
| 22-09-22 | Crossformer | |
ICLR 2023 | Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting | Crossformer |
| 22-09-22 | PatchTST🌟 | |
ICLR 2023 | A Time Series is Worth 64 Words: Long-term Forecasting with Transformers | PatchTST |
| 22-11-29 | AirFormer |  |
AAAI 2023 | AirFormer: Predicting Nationwide Air Quality in China with Transformers | AirFormer |
| 22-12-06 | TVT | |
Arxiv 2022 | A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting | None |
| 23-01-05 | Conformer | |
ICDE 2023 | Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution | Conformer |
| 23-01-19 | PDFormer | |
AAAI 2023 | PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction | PDFormer |
| 23-03-01 | ViTST | |
NIPS 2023 | Time Series as Images: Vision Transformer for Irregularly Sampled Time Series | ViTST |
| 23-05-20 | CARD | |
ICLR 2024 | Make Transformer Great Again for Time Series Forecasting: Channel Aligned Robust Dual Transformer | CARD |
| 23-05-24 | JTFT | |
Arxiv 2023 | A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting | None |
| 23-05-30 | HSTTN | |
IJCAI 2023 | Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer | None |
| 23-05-30 | Client | |
Arxiv 2023 | Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting | Client |
| 23-05-30 | Taylorformer | |
Arxiv 2023 | Taylorformer: Probabilistic Predictions for Time Series and other Processes | Taylorformer |
| 23-06-05 | Corrformer🌟 | |
NMI 2023 | Interpretable weather forecasting for worldwide stations with a unified deep model | Corrformer |
| 23-06-14 | GCformer | |
CIKM 2023 | GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting | GCformer |
| 23-07-04 | SageFormer | |
IoT 2024 | SageFormer: Series-Aware Graph-Enhanced Transformers for Multivariate Time Series Forecasting | SageFormer |
| 23-07-10 | DifFormer | |
TPAMI 2023 | DifFormer: Multi-Resolutional Differencing Transformer With Dynamic Ranging for Time Series Analysis | None |
| 23-07-27 | HUTFormer | |
Arxiv 2023 | HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting | None |
| 23-08-07 | DSformer | |
CIKM 2023 | DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction | None |
| 23-08-09 | SBT | |
KDD 2023 | Sparse Binary Transformers for Multivariate Time Series Modeling | None |
| 23-08-09 | PETformer | |
Arxiv 2023 | PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer | None |
| 23-10-02 | TACTiS-2 | |
ICLR 2024 | TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series | None |
| 23-10-03 | PrACTiS | |
Arxiv 2023 | PrACTiS: Perceiver-Attentional Copulas for Time Series | None |
| 23-10-10 | iTransformer | |
ICLR 2024 | iTransformer: Inverted Transformers Are Effective for Time Series Forecasting | iTransformer |
| 23-10-26 | ContiFormer | |
NIPS 2023 | ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling | None |
| 23-10-31 | BasisFormer | |
NIPS 2023 | BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis | basisformer |
| 23-11-07 | MTST | |
Arxiv 2023 | Multi-resolution Time-Series Transformer for Long-term Forecasting | None |
| 23-11-30 | MultiResFormer | |
Arxiv 2023 | MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting | None |
| 23-12-10 | FPPformer | |
IOT 2023 | Take an Irregular Route: Enhance the Decoder of Time-Series Forecasting Transformer | FPPformer |
| 23-12-11 | Dozerformer | |
Arxiv 2023 | Dozerformer: Sequence Adaptive Sparse Transformer for Multivariate Time Series Forecasting | None |
| 23-12-11 | CSformer | |
Arxiv 2023 | Dance of Channel and Sequence: An Efficient Attention-Based Approach for Multivariate Time Series Forecasting | None |
| 23-12-23 | MASTER | |
AAAI 2024 | MASTER: Market-Guided Stock Transformer for Stock Price Forecasting | MASTER |
| 23-12-30 | PCA+former | |
Arxiv 2023 | Transformer Multivariate Forecasting: Less is More? | None |
| 24-01-16 | |
ICLR 2024 | Periodicity Decoupling Framework for Long-term Series Forecasting | ||
| 24-01-16 | Pathformer | |
ICLR 2024 | Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting | None |
| 24-01-16 | VQ-TR | |
ICLR 2024 | VQ-TR: Vector Quantized Attention for Time Series Forecasting | None |
| 24-01-22 | HDformer | |
Arxiv 2024 | The Bigger the Better? Rethinking the Effective Model Scale in Long-term Time Series Forecasting | None |
| 24-02-04 | Minusformer | |
Arxiv 2024 | Minusformer: Improving Time Series Forecasting by Progressively Learning Residuals | Minusformer |
| 24-02-08 | AttnEmbed | |
Arxiv 2024 | Attention as Robust Representation for Time Series Forecasting | AttnEmbed |
| 24-02-15 | SAMformer | |
Arxiv 2024 | Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention | SAMformer |
| 24-02-25 | PDETime | |
Arxiv 2024 | PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations | None |
| 24-02-29 | TimeXer | |
Arxiv 2024 | TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables | None |
| Date | Method | Type | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|---|
| 17-03-21 | LSTNet🌟 | |
SIGIR 2018 | Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks | LSTNet |
| 17-04-07 | DA-RNN | |
IJCAI 2017 | A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction | DARNN |
| 17-04-13 | DeepAR🌟 | |
IJoF 2019 | DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks | DeepAR |
| 17-11-29 | MQRNN | |
NIPSW 2017 | A Multi-Horizon Quantile Recurrent Forecaster | MQRNN |
| 18-06-23 | mWDN | |
KDD 2018 | Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis | mWDN |
| 18-09-06 | MTNet | |
AAAI 2019 | A Memory-Network Based Solution for Multivariate Time-Series Forecasting | MTNet |
| 19-05-28 | DF-Model | |
ICML 2019 | Deep Factors for Forecasting | None |
| 19-07-18 | ESLSTM | |
IJoF 2020 | A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting | None |
| 19-07-25 | MH-TAL | |
KDD 2019 | Multi-Horizon Time Series Forecasting with Temporal Attention Learning | None |
| 21-11-22 | CRU | |
ICML 2022 | Modeling Irregular Time Series with Continuous Recurrent Units | CRU |
| 22-05-16 | C2FAR | |
NIPS 2022 | C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting | C2FAR |
| 23-06-02 | RNN-ODE-Adap | |
Arxiv 2023 | Neural Differential Recurrent Neural Network with Adaptive Time Steps | RNN_ODE_Adap |
| 23-08-22 | SegRNN | |
Arxiv 2023 | SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting | SegRNN |
| 23-10-05 | PA-RNN | |
NIPS 2023 | Sparse Deep Learning for Time Series Data: Theory and Applications | None |
| 23-11-03 | WITRAN | |
NIPS 2023 | WITRAN: Water-wave Information Transmission and Recurrent Acceleration Network for Long-range Time Series Forecasting | WITRAN |
| 23-12-14 | DAN | |
AAAI 2024 | Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting | DAN |
| 23-12-22 | SutraNets | |
NIPS 2023 | SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecasting | None |
| 24-01-17 | RWKV-TS | |
Arxiv 2024 | RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks | RWKV-TS |
| Date | Method | Type | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|---|
| 19-05-24 | NBeats🌟 | |
ICLR 2020 | N-BEATS: Neural Basis Expansion Analysis For Interpretable Time Series Forecasting | NBeats |
| 21-04-12 | NBeatsX | |
IJoF 2022 | Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx | NBeatsX |
| 22-01-30 | N-HiTS🌟 | |
AAAI 2023 | N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting | N-HiTS |
| 22-05-15 | DEPTS | |
ICLR 2022 | DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting | DEPTS |
| 22-05-24 | FreDo | |
Arxiv 2022 | FreDo: Frequency Domain-based Long-Term Time Series Forecasting | None |
| 22-05-26 | DLinear🌟 | |
AAAI 2023 | Are Transformers Effective for Time Series Forecasting? | DLinear |
| 22-06-24 | TreeDRNet | |
Arxiv 2022 | TreeDRNet: A Robust Deep Model for Long Term Time Series Forecasting | None |
| 22-07-04 | LightTS | |
Arxiv 2022 | Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures | LightTS |
| 22-08-10 | STID | |
CIKM 2022 | Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting | STID |
| 23-01-30 | SimST | |
Arxiv 2023 | Do We Really Need Graph Neural Networks for Traffic Forecasting? | None |
| 23-02-09 | MTS-Mixers | |
Arxiv 2023 | MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing | MTS-Mixers |
| 23-03-10 | TSMixer | |
Arxiv 2023 | TSMixer: An all-MLP Architecture for Time Series Forecasting | None |
| 23-04-17 | TiDE🌟 | |
Arxiv 2023 | Long-term Forecasting with TiDE: Time-series Dense Encoder | TiDE |
| 23-05-18 | RTSF | |
Arxiv 2023 | Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping | RTSF |
| 23-05-30 | Koopa🌟 | |
NIPS 2023 | Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors | Koopa |
| 23-06-14 | CI-TSMixer | |
KDD 2023 | TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting | None |
| 23-07-06 | FITS🌟 | |
ICLR 2024 | FITS: Modeling Time Series with 10k Parameters | FITS |
| 23-08-14 | ST-MLP | |
Arxiv 2023 | ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting | None |
| 23-08-25 | TFDNet | |
Arxiv 2023 | TFDNet: Time-Frequency Enhanced Decomposed Network for Long-term Time Series Forecasting | None |
| 23-11-10 | FreTS | |
NIPS 2023 | Frequency-domain MLPs are More Effective Learners in Time Series Forecasting | FreTS |
| 23-12-22 | STL | |
Arxiv 2023 | Spatiotemporal-Linear: Towards Universal Multivariate Time Series Forecasting | None |
| 24-01-04 | U-Mixer | |
AAAI 2024 | U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting | None |
| 24-01-16 | TimeMixer | |
ICLR 2024 | TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting | None |
| 24-02-16 | RPMixer | |
Arxiv 2024 | Random Projection Layers for Multidimensional Time Sires Forecasting | None |
| 24-02-20 | IDEA | |
Arxiv 2024 | When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting | None |
| Date | Method | Type | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|---|
| 19-05-09 | DeepGLO🌟 | |
NIPS 2019 | Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting | deepglo |
| 19-05-22 | DSANet | |
CIKM 2019 | DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting | DSANet |
| 19-12-11 | MLCNN | |
AAAI 2020 | Towards Better Forecasting by Fusing Near and Distant Future Visions | MLCNN |
| 21-06-17 | SCINet | |
NIPS 2022 | SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction | SCINet |
| 22-09-22 | MICN | |
ICLR 2023 | MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting | MICN |
| 22-09-22 | TimesNet🌟 | |
ICLR 2023 | TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis | TimesNet |
| 23-02-23 | LightCTS | |
SIGMOD 2023 | LightCTS: A Lightweight Framework for Correlated Time Series Forecasting | LightCTS |
| 23-05-25 | TLNets | |
Arxiv 2023 | TLNets: Transformation Learning Networks for long-range time-series prediction | TLNets |
| 23-06-04 | Cross-LKTCN | |
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 | |
Arxiv 2023 | MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting | None |
| 23-06-19 | FDNet | |
KBS 2023 | FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting | FDNet |
| 23-10-01 | PatchMixer | |
Arxiv 2023 | PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting | PatchMixer |
| 23-11-01 | WinNet | |
Arxiv 2023 | WinNet:time series forecasting with a window-enhanced period extracting and interacting | None |
| 23-11-27 | ModernTCN🌟 | |
ICLR 2024 | ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis | None |
| 23-11-27 | UniRepLKNet | |
Arxiv 2023 | UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition | UniRepLKNet |
| Date | Method | Type | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|---|
| 17-09-14 | STGCN🌟 | |
IJCAI 2018 | Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting | STGCN |
| 19-05-31 | Graph WaveNet | |
IJCAI 2019 | Graph WaveNet for Deep Spatial-Temporal Graph Modeling | Graph-WaveNet |
| 19-07-17 | ASTGCN | |
AAAI 2019 | Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting | ASTGCN |
| 20-04-03 | SLCNN | |
AAAI 2020 | Spatio-Temporal Graph Structure Learning for Traffic Forecasting | None |
| 20-04-03 | GMAN | |
AAAI 2020 | GMAN: A Graph Multi-Attention Network for Traffic Prediction | GMAN |
| 20-05-03 | MTGNN🌟 | |
KDD 2020 | Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks | MTGNN |
| 21-03-13 | StemGNN🌟 | |
NIPS 2020 | Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting | StemGNN |
| 22-05-16 | TPGNN | |
NIPS 2022 | Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks | TPGNN |
| 22-06-18 | D2STGNN | |
VLDB 2022 | Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting | D2STGNN |
| 23-05-12 | DDGCRN | |
PR 2023 | A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting | DDGCRN |
| 23-07-10 | NexuSQN | |
Arxiv 2023 | Nexus sine qua non: Essentially connected neural networks for spatial-temporal forecasting of multivariate time series | None |
| 23-11-10 | FourierGNN | |
NIPS 2023 | FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective | FourierGNN |
| 23-12-05 | SAMSGL | |
TETCI 2023 | SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting | None |
| 23-12-27 | TGCRN | |
ICDE 2024 | Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting | None |
| 23-12-27 | FCDNet | |
Arxiv 2023 | FCDNet: Frequency-Guided Complementary Dependency Modeling for Multivariate Time-Series Forecasting | FCDNet |
| 23-12-31 | MSGNet | |
AAAI 2024 | MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting | MSGNet |
| 24-01-15 | RGDAN | |
NN 2024 | RGDAN: A random graph diffusion attention network for traffic prediction | RGDAN |
| 24-01-16 | BiaTCGNet | |
ICLR 2024 | Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values | BiaTCGNet |
| 24-01-24 | TMP | |
AAAI 2024 | Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence | None |
| 24-02-16 | HD-TTS | |
Arxiv 2024 | Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling | None |
| 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 |
| 19-08-10 | DSSMF | IJCAI 2019 | Learning Interpretable Deep State Space Model for Probabilistic 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 | LS4 |
| 24-02-18 | Attraos | Arxiv 2024 | Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective | 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 |
| 22-12-28 | 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 |
| 23-07-21 | TSDiff | NIPS 2023 | Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting | TSDiff |
| 24-01-16 | FTS-Diffusion | ICLR 2024 | Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns | None |
| 24-01-16 | MG-TSD | ICLR 2024 | MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process | None |
| 24-01-16 | TMDM | ICLR 2024 | Transformer-Modulated Diffusion Models for Probabilistic Multivariate Time Series Forecasting | None |
| 24-01-16 | mr-Diff | ICLR 2024 | Multi-Resolution Diffusion Models for Time Series Forecasting | None |
| 24-01-16 | Diffusion-TS | ICLR 2024 | Diffusion-TS: Interpretable Diffusion for General Time Series Generation | None |
| 24-01-16 | SpecSTG | Arxiv 2024 | SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting | SpecSTG |
| 24-01-30 | IN-Flow | Arxiv 2024 | Addressing Distribution Shift in Time Series Forecasting with Instance Normalization Flows | None |
| Date | Method | Type | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|---|
| 17-05-25 | ND | |
TNNLS 2017 | Neural Decomposition of Time-Series Data for Effective Generalization | None |
| 17-08-25 | Prophet🌟 | |
TAS 2018 | Forecasting at Scale | Prophet |
| 22-07-13 | DeepTime | |
ICML 2023 | Learning Deep Time-index Models for Time Series Forecasting | DeepTime |
| 23-06-09 | TimeFlow | |
Arxiv 2023 | Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations | None |
| 24-01-16 | DAM🌟 | |
ICLR 2024 | DAM: A Foundation Model for Forecasting | 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 |
| 23-05-26 | BetterBatch | Arxiv 2023 | Better Batch for Deep Probabilistic Time Series Forecasting | None |
| 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 |
| 23-07-18 | Look_Ahead | SIGIR 2023 | Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features | Look_Ahead |
| 23-09-14 | QFCV | Arxiv 2023 | Uncertainty Intervals for Prediction Errors in Time Series Forecasting | QFCV |
| 23-10-09 | PeTS | Arxiv 2023 | Performative Time-Series Forecasting | PeTS |
| 23-10-23 | EDAIN | Arxiv 2023 | Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks | EDAIN |
| 23-11-19 | TimeSQL | Arxiv 2023 | TimeSQL: Improving Multivariate Time Series Forecasting with Multi-Scale Patching and Smooth Quadratic Loss | None |
| 24-01-16 | LIFT | ICLR 2024 | Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators | None |
| 24-01-16 | RobustTSF | ICLR 2024 | RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies | RobustTSF |
| 24-02-04 | FreDF | Arxiv 2024 | FreDF: Learning to Forecast in Frequency Domain | FreDF |
| 24-02-20 | Leddam | Arxiv 2024 | Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling | None |
| Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|
| 22-09-20 | PromptCast | TKDE 2023 | PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting | PISA |
| 23-02-23 | FPT 🌟 | NIPS 2023 | One Fits All: Power General Time Series Analysis by Pretrained LM | One-Fits-All |
| 23-05-17 | LLMTime | NIPS 2023 | Large Language Models Are Zero-Shot Time Series Forecasters | LLMTime |
| 23-08-16 | TEST | ICLR 2024 | TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series | None |
| 23-08-16 | LLM4TS | Arxiv 2023 | LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs | None |
| 23-10-03 | Time-LLM | ICLR 2024 | Time-LLM: Time Series Forecasting by Reprogramming Large Language Models | None |
| 23-10-08 | TEMPO | ICLR 2024 | TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting | None |
| 23-10-12 | Lag-Llama | Arxiv 2023 | Lag-Llama: Towards Foundation Models for Time Series Forecasting | Lag-Llama |
| 23-10-15 | UniTime | Arxiv 2023 | UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting | None |
| 23-11-03 | ForecastPFN | NIPS 2023 | ForecastPFN: Synthetically-Trained Zero-Shot Forecasting | ForecastPFN |
| 23-11-24 | FPT++ 🌟 | Arxiv 2023 | One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors | GPT4TS_Adapter |
| 24-01-18 | ST-LLM | Arxiv 2024 | Spatial-Temporal Large Language Model for Traffic Prediction | None |
| 24-02-01 | LLMICL | Arxiv 2024 | LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law | LLMICL |
| 24-02-04 | AutoTimes | Arxiv 2024 | AutoTimes: Autoregressive Time Series Forecasters via Large Language Models | AutoTimes |
| 24-02-16 | TSFwithLLM | Arxiv 2024 | Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities | None |
| 24-02-25 | LSTPrompt | Arxiv 2024 | LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting | LSTPrompt |
| Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|
| 20-10-06 | TST | KDD 2021 | A Transformer-based Framework for Multivariate Time Series Representation Learning | mvts_transformer |
| 21-09-29 | CoST | ICLR 2022 | CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting | CoST |
| 22-05-16 | LaST | NIPS 2022 | LaST: Learning Latent Seasonal-Trend Representations for Time Series Forecasting | LaST |
| 22-06-18 | STEP | KDD 2022 | Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting | STEP |
| 23-02-02 | SimMTM | NIPS 2023 | SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling | SimMTM |
| 23-02-07 | DBPM | ICLR 2024 | Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach | None |
| 23-03-01 | TimeMAE | Arxiv 2023 | TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders | TimeMAE |
| 23-08-02 | Floss | Arxiv 2023 | Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach | floss |
| 23-12-01 | STD_MAE | Arxiv 2023 | Spatio-Temporal-Decoupled Masked Pre-training for Traffic Forecasting | STD_MAE |
| 23-12-25 | TimesURL | AAAI 2024 | TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning | None |
| 24-01-08 | TTMs | Arxiv 2024 | TTMs: Fast Multi-level Tiny Time Mixers for Improved Zero-shot and Few-shot Forecasting of Multivariate Time Series | None |
| 24-01-16 | SoftCLT | ICLR 2024 | Soft Contrastive Learning for Time Series | None |
| 24-01-16 | PITS | ICLR 2024 | Learning to Embed Time Series Patches Independently | PITS |
| 24-01-16 | T-Rep | ICLR 2024 | T-Rep: Representation Learning for Time Series using Time-Embeddings | None |
| 24-01-16 | AutoTCL | ICLR 2024 | Parametric Augmentation for Time Series Contrastive Learning | None |
| 24-01-16 | AutoCon | ICLR 2024 | Self-Supervised Contrastive Forecasting | AutoCon |
| 24-01-29 | MLEM | Arxiv 2024 | Self-Supervised Learning in Event Sequences: A Comparative Study and Hybrid Approach of Generative Modeling and Contrastive Learning | MLEM |
| 24-02-04 | Timer | Arxiv 2024 | Timer: Transformers for Time Series Analysis at Scale | Timer |
| 24-02-04 | TimeSiam | Arxiv 2024 | TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling | None |
| 24-02-04 | MOIRAI | Arxiv 2024 | Unified Training of Universal Time Series Forecasting Transformers | None |
| 24-02-14 | GTT | Arxiv 2024 | Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction | GTT |
| 24-02-26 | TOTEM | Arxiv 2024 | TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis | TOTEM |
| 24-02-26 | GPHT | Arxiv 2024 | Generative Pretrained Hierarchical Transformer for Time Series Forecasting | None |
| 24-02-29 | UniTS | Arxiv 2024 | UniTS: Building a Unified Time Series Model | UniTS |
| Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|
| 21-02-13 | DAF | ICML 2022 | Domain Adaptation for Time Series Forecasting via Attention Sharing | DAF |
| Date | Method | Type | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|---|
| 22-02-23 | FSNet | |
ICLR 2023 | Learning Fast and Slow for Online Time Series Forecasting | FSNet |
| 23-09-22 | OneNet | |
NIPS 2023 | OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling | OneNet |
| 23-09-25 | MemDA | |
CIKM 2023 | MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation | None |
| 24-01-08 | ADCSD | |
Arxiv 2024 | Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting | ADCSD |
| 24-02-03 | TSF-HD | |
Arxiv 2024 | A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edge | TSF-HD |
| 24-02-20 | SKI-CL | |
Arxiv 2024 | Structural Knowledge Informed Continual Multivariate Time Series Forecasting | None |
| 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 | WaveBound |
| 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 |
| 24-01-16 | STanHop-Net | ICLR 2024 | STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction | None |
| 24-02-02 | SNN | Arxiv 2024 | Efficient and Effective Time-Series Forecasting with Spiking Neural Networks | None |