Awesome Time Series Forecasting/Prediction Papers

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.

Some Additional Information.

🚩 2023/11/1: I have marked some recommended papers with 🌟 (Just my personal preference 😉).

🚩 2023/11/1: I have added a new category : models specifically designed for irregular time series.

🚩 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.

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	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

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
21-09-17	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	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
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	Arxiv 2023	SageFormer: Series-Aware Graph-Enhanced Transformers for Multivariate Time Series Forecasting	None
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	PDF	ICLR 2024	Periodicity Decoupling Framework for Long-term Series Forecasting	None
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

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
19-07-18	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	None
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

MLP.

Date	Method	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

TCN/CNN.

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
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

GNN.

Date	Method	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
23-07-10	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
24-01-16	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

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
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

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
22-12-28	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

Time-index.

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
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

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
23-04-19	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

LLM (Large Language Model).

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

Pretrain & Representation.

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
24-01-29	MLEM	Arxiv 2024	Self-Supervised Learning in Event Sequences: A Comparative Study and Hybrid Approach of Generative Modeling and Contrastive Learning	MLEM

Domain Adaptation.

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

Online.

Date	Method	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

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	WaveBound
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
24-01-16	STanHop-Net	ICLR 2024	STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction	None

ddz16/TSFpaper

ddz16

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