Time Series Forecasting

Time Series Forecasting is the task of fitting a model to historical, time-stamped data in order to predict future values. Traditional approaches include moving average, exponential smoothing, and ARIMA, though models as various as RNNs, Transformers, or XGBoost can also be applied. The most popular benchmark is the ETTh1 dataset. Models are typically evaluated using the Mean Square Error (MSE) or Root Mean Square Error (RMSE).

( Image credit: ThaiBinh Nguyen )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 151–175 of 1609 papers

Title	Date	Tasks	Status	Hype	Score
ETSformer: Exponential Smoothing Transformers for Time-series Forecasting	Feb 3, 2022	Time SeriesTime Series Analysis	CodeCode Available	2	5
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting	Jan 30, 2022	Time SeriesTime Series Analysis	CodeCode Available	2	5
HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?	Jun 20, 2024	BenchmarkingPoint Processes	CodeCode Available	2	5
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection	Jul 7, 2023	Anomaly DetectionImputation	CodeCode Available	2	5
Patch-wise Structural Loss for Time Series Forecasting	Mar 2, 2025	Time SeriesTime Series Forecasting	CodeCode Available	2	5
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges	Apr 24, 2024	Drug DesignInductive Bias	CodeCode Available	2	5
Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis	Jun 12, 2024	Time SeriesTime Series Analysis	CodeCode Available	2	5
DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks	Mar 21, 2025	Time Series Forecasting	CodeCode Available	1	5
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities	Feb 8, 2022	Domain AdaptationGraph Mining	CodeCode Available	1	5
Graph Neural Controlled Differential Equations for Traffic Forecasting	Dec 7, 2021	Spatio-Temporal ForecastingTime Series Forecasting	CodeCode Available	1	5
Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting	May 30, 2023	Spatio-Temporal ForecastingTime Series Forecasting	CodeCode Available	1	5
Do We Really Need Deep Learning Models for Time Series Forecasting?	Jan 6, 2021	Deep Learningregression	CodeCode Available	1	5
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting	May 30, 2023	ClusteringTime Series	CodeCode Available	1	5
Graph Neural Networks for Improved El Niño Forecasting	Dec 2, 2020	Deep LearningMultivariate Time Series Forecasting	CodeCode Available	1	5
Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting	Feb 18, 2025	DisentanglementTime Series	CodeCode Available	1	5
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting	Feb 22, 2023	Time SeriesTime Series Analysis	CodeCode Available	1	5
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series Forecasting	Jun 22, 2022	Time SeriesTime Series Analysis	CodeCode Available	1	5
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting	Dec 11, 2024	Adversarial AttackTime Series	CodeCode Available	1	5
AA-Forecast: Anomaly-Aware Forecast for Extreme Events	Aug 21, 2022	Anomaly ForecastingMultivariate Time Series Forecasting	CodeCode Available	1	5
Discovering Predictable Latent Factors for Time Series Forecasting	Mar 18, 2023	Time SeriesTime Series Forecasting	CodeCode Available	1	5
Adversarial Sparse Transformer for Time Series Forecasting	Dec 1, 2020	Multivariate Time Series ForecastingPrediction	CodeCode Available	1	5
D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting	Mar 26, 2024	Time SeriesTime Series Forecasting	CodeCode Available	1	5
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting	Jul 6, 2017	DecoderMultivariate Time Series Forecasting	CodeCode Available	1	5
Discrete Graph Structure Learning for Forecasting Multiple Time Series	Jan 18, 2021	Graph structure learningTime Series	CodeCode Available	1	5
An Experimental Review on Deep Learning Architectures for Time Series Forecasting	Mar 22, 2021	Deep LearningTime Series	CodeCode Available	1	5

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All datasets ETTh1 (336) Multivariate ETTh1 (720) Multivariate ETTh2 (336) Multivariate ETTh2 (720) Multivariate ETTh1 (192) Multivariate ETTh2 (192) Multivariate ETTh2 (96) Multivariate ETTh1 (96) Multivariate Weather (192)ETTh1 (720) Univariate Weather (96)Electricity (96)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Informer	MSE	0.88	—	Unverified
2	QuerySelector	MSE	0.85	—	Unverified
3	Transformer	MSE	0.83	—	Unverified
4	Aaren	MSE	0.65	—	Unverified
5	RPMixer	MSE	0.52	—	Unverified
6	MOIRAILarge	MSE	0.51	—	Unverified
7	ATFNet	MSE	0.51	—	Unverified
8	Autoformer	MSE	0.51	—	Unverified
9	SCINet	MSE	0.5	—	Unverified
10	S-Mamba	MSE	0.49	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	QuerySelector	MSE	1.12	—	Unverified
2	Transformer	MSE	1.11	—	Unverified
3	Informer	MSE	0.94	—	Unverified
4	GLinear	MSE	0.59	—	Unverified
5	SCINet	MSE	0.54	—	Unverified
6	MoLE-DLinear	MSE	0.51	—	Unverified
7	PRformer	MSE	0.49	—	Unverified
8	TEFN	MSE	0.48	—	Unverified
9	DLinear	MSE	0.47	—	Unverified
10	FiLM	MSE	0.47	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Transformer	MSE	2.66	—	Unverified
2	QuerySelector	MSE	2.32	—	Unverified
3	Informer	MSE	1.67	—	Unverified
4	DLinear	MSE	0.45	—	Unverified
5	TEFN	MSE	0.42	—	Unverified
6	MoLE-DLinear	MSE	0.42	—	Unverified
7	FiLM	MSE	0.38	—	Unverified
8	MoLE-RLinear	MSE	0.37	—	Unverified
9	SCINet	MSE	0.37	—	Unverified
10	PRformer	MSE	0.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Transformer	MSE	3.18	—	Unverified
2	QuerySelector	MSE	3.07	—	Unverified
3	Informer	MSE	2.34	—	Unverified
4	DLinear	MSE	0.61	—	Unverified
5	MoLE-DLinear	MSE	0.61	—	Unverified
6	SCINet	MSE	0.48	—	Unverified
7	FiLM	MSE	0.44	—	Unverified
8	TEFN	MSE	0.43	—	Unverified
9	TiDE	MSE	0.42	—	Unverified
10	MoLE-RLinear	MSE	0.41	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MoLE-DLinear	MSE	0.45	—	Unverified
2	TEFN	MSE	0.43	—	Unverified
3	FiLM	MSE	0.41	—	Unverified
4	PatchTST/64	MSE	0.41	—	Unverified
5	TiDE	MSE	0.41	—	Unverified
6	NLinear	MSE	0.41	—	Unverified
7	DiPE-Linear	MSE	0.41	—	Unverified
8	DLinear	MSE	0.41	—	Unverified
9	RLinear	MSE	0.4	—	Unverified
10	MoLE-RLinear	MSE	0.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DLinear	MSE	0.38	—	Unverified
2	TEFN	MSE	0.38	—	Unverified
3	MoLE-DLinear	MSE	0.36	—	Unverified
4	FiLM	MSE	0.36	—	Unverified
5	NLinear	MSE	0.34	—	Unverified
6	PatchTST/64	MSE	0.34	—	Unverified
7	MoLE-RLinear	MSE	0.34	—	Unverified
8	TiDE	MSE	0.33	—	Unverified
9	LTBoost (drop_last=false)	MSE	0.33	—	Unverified
10	PRformer	MSE	0.33	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DLinear	MSE	0.29	—	Unverified
2	TEFN	MSE	0.29	—	Unverified
3	MoLE-DLinear	MSE	0.29	—	Unverified
4	FiLM	MSE	0.28	—	Unverified
5	NLinear	MSE	0.28	—	Unverified
6	TSMixer	MSE	0.28	—	Unverified
7	DiPE-Linear	MSE	0.28	—	Unverified
8	PatchTST/64	MSE	0.27	—	Unverified
9	MoLE-RLinear	MSE	0.27	—	Unverified
10	TiDE	MSE	0.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TEFN	MSE	0.38	—	Unverified
2	MoLE-DLinear	MSE	0.38	—	Unverified
3	TiDE	MSE	0.38	—	Unverified
4	MoLE-RLinear	MSE	0.38	—	Unverified
5	FiLM	MSE	0.37	—	Unverified
6	PatchTST/64	MSE	0.37	—	Unverified
7	DiPE-Linear	MSE	0.37	—	Unverified
8	TSMixer	MSE	0.37	—	Unverified
9	RLinear	MSE	0.37	—	Unverified
10	TTM	MSE	0.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TEFN	MSE	0.23	—	Unverified
2	DLinear	MSE	0.22	—	Unverified