Skip to content
/ TSFpaper Public
forked from ddz16/TSFpaper

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.

1 star 204 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

tandakun/TSFpaper

BranchesTags
 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

65 Commits
.gitattributes
.gitattributes
 
 
README.md
README.md
 
 

Repository files navigation

TSF Paper

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.

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 are not considered, univariate time series forecasting involves predicting the future of one variable with the history of one 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. Spatio-temporal forecasting is often used in traffic and weather forecasting, and it adds a spatial dimension compared to univariate and multivariate forecasting.

  • 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).

Survey.

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
23-05-01 Diffusion Arxiv 2023 Diffusion Models for Time Series Applications: A Survey None

Transformer.

Date Method 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
21-10-05 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
23-05-20 CARD Arxiv 2023 Make Transformer Great Again for Time Series Forecasting: Channel Aligned Robust Dual Transformer None
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

RNN.

Date Method 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
19-07-01 MH-RNN KDD 2019 Multi-Horizon Time Series Forecasting with Temporal Attention Learning 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
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 None

MLP.

Date Method 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
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
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 Arxiv 2023 Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors None

TCN.

Date Method 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

SSM (State Space Model)

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

GNN (Spatio-Temporal Modeling).

Date Method Conference Paper Title and Paper Interpretation (In Chinese) Code

Generation Model.

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

Time-index.

Date Method Conference Paper Title and Paper Interpretation (In Chinese) Code
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

Plug and Play (Model-Agnostic).

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
23-05-28 PALS Arxiv 2023 Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers None

Pretrain & Representation.

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

Theory.

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

Other.

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

About

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.

Resources

Readme
Activity

Stars

1 star

Watchers

0 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published