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 601–625 of 1609 papers

Title	Date	Tasks	Status
Bayesian Time Series Forecasting with Change Point and Anomaly Detection	Jan 1, 2018	Anomaly DetectionChange Point Detection	—Unverified
Diffusion Models for Time Series Applications: A Survey	May 1, 2023	ImputationSurvey	—Unverified
An Attention Free Long Short-Term Memory for Time Series Forecasting	Sep 20, 2022	PredictionTime Series	—Unverified
A Differential Attention Fusion Model Based on Transformer for Time Series Forecasting	Feb 23, 2022	Time SeriesTime Series Analysis	—Unverified
HiPPO-KAN: Efficient KAN Model for Time Series Analysis	Oct 19, 2024	Time SeriesTime Series Analysis	—Unverified
Diffusion-based Time Series Forecasting for Sewerage Systems	Jun 10, 2025	Prediction IntervalsTime Series	—Unverified
Diffusion-Based Forecasting for Uncertainty-Aware Model Predictive Control	Mar 19, 2025	Decision MakingModel Predictive Control	—Unverified
Balancing Computational Efficiency and Forecast Error in Machine Learning-based Time-Series Forecasting: Insights from Live Experiments on Meteorological Nowcasting	Sep 26, 2023	Computational EfficiencyDrift Detection	—Unverified
DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models	Mar 21, 2024	DenoisingManagement	—Unverified
Differential Convolutional Fuzzy Time Series Forecasting	May 15, 2023	Time SeriesTime Series Forecasting	—Unverified
Avocado Price Prediction Using a Hybrid Deep Learning Model: TCN-MLP-Attention Architecture	May 15, 2025	ImputationTime Series Forecasting	—Unverified
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout	Nov 27, 2022	Anomaly DetectionTime Series	—Unverified
A Deep-Unfolding Approach to RIS Phase Shift Optimization Via Transformer-Based Channel Prediction	Feb 25, 2025	Multivariate Time Series ForecastingPrediction	—Unverified
Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecasting	Mar 30, 2021	Time SeriesTime Series Analysis	—Unverified
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures	Jun 14, 2021	Language ModelingLanguage Modelling	—Unverified
DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting	May 14, 2024	ClusteringCollaborative Filtering	—Unverified
A Comprehensive Forecasting Framework based on Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment	Dec 19, 2024	Bayesian OptimizationDemand Forecasting	—Unverified
Development and Evaluation of Recurrent Neural Network based Models for Hourly Traffic Volume and AADT Prediction	Aug 15, 2018	ImputationMissing Values	—Unverified
Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction	Aug 22, 2024	modelTime Series	—Unverified
The cost of ensembling: is it always worth combining?	Jun 5, 2025	Ensemble LearningTime Series Forecasting	—Unverified
Deterministic Reservoir Computing for Chaotic Time Series Prediction	Jan 26, 2025	PredictionTime Series	—Unverified
Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Deep Learning-Based Time Series Forecasting	Nov 9, 2022	Time SeriesTime Series Analysis	—Unverified
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning	Mar 26, 2022	Neural Architecture SearchTime Series	—Unverified
Characterizing the memory capacity of transmon qubit reservoirs	Apr 15, 2020	Open-Ended Question AnsweringTime Series Analysis	—Unverified
Analytics of Business Time Series Using Machine Learning and Bayesian Inference	May 25, 2022	Bayesian InferenceBIG-bench Machine Learning	—Unverified

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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	MoLE-DLinear	MSE	0.61	—	Unverified
5	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	LTBoost (drop_last=false)	MSE	0.33	—	Unverified
9	PRformer	MSE	0.33	—	Unverified
10	TiDE	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	MoLE-RLinear	MSE	0.38	—	Unverified
4	TiDE	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