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

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Showing 601–625 of 1609 papers

Title	Date	Tasks	Status
TSKANMixer: Kolmogorov-Arnold Networks with MLP-Mixer Model for Time Series Forecasting	Feb 25, 2025	Kolmogorov-Arnold NetworksManagement	—Unverified
A Deep-Unfolding Approach to RIS Phase Shift Optimization Via Transformer-Based Channel Prediction	Feb 25, 2025	Multivariate Time Series ForecastingPrediction	—Unverified
Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators	Feb 24, 2025	Language ModelingLanguage Modelling	—Unverified
Predicting Bad Goods Risk Scores with ARIMA Time Series: A Novel Risk Assessment Approach	Feb 23, 2025	Decision MakingTime Series	—Unverified
Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning	Feb 21, 2025	Contrastive LearningMultivariate Time Series Forecasting	—Unverified
Spatiotemporal Forecasting in Climate Data Using EOFs and Machine Learning Models: A Case Study in Chile	Feb 21, 2025	Dynamic Time WarpingTime Series	—Unverified
CoRe: Coherency Regularization for Hierarchical Time Series	Feb 21, 2025	Missing ValuesTime Series	—Unverified
A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting	Feb 21, 2025	Dimensionality ReductionTime Series	—Unverified
Explaining the Success of Nearest Neighbor Methods in Prediction	Feb 21, 2025	Predictionregression	—Unverified
Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting	Feb 20, 2025	AllSelf-Supervised Learning	—Unverified
TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation	Feb 20, 2025	Data AugmentationKnowledge Distillation	—Unverified
Position: There are no Champions in Long-Term Time Series Forecasting	Feb 19, 2025	BenchmarkingPosition	—Unverified
Learning Novel Transformer Architecture for Time-series Forecasting	Feb 19, 2025	Neural Architecture SearchTime Series	—Unverified
Lightweight Online Adaption for Time Series Foundation Model Forecasts	Feb 18, 2025	Time SeriesTime Series Forecasting	—Unverified
S2TX: Cross-Attention Multi-Scale State-Space Transformer for Time Series Forecasting	Feb 17, 2025	MambaTime Series	—Unverified
On Creating a Causally Grounded Usable Rating Method for Assessing the Robustness of Foundation Models Supporting Time Series	Feb 17, 2025	Model SelectionStock Price Prediction	—Unverified
IMTS-Mixer: Mixer-Networks for Irregular Multivariate Time Series Forecasting	Feb 17, 2025	Computational EfficiencyMissing Values	—Unverified
Vision-Enhanced Time Series Forecasting via Latent Diffusion Models	Feb 16, 2025	Image ReconstructionTime Series	—Unverified
Forecasting time series with constraints	Feb 14, 2025	Additive modelsBenchmarking	CodeCode Available
HADL Framework for Noise Resilient Long-Term Time Series Forecasting	Feb 14, 2025	Computational EfficiencyDecision Making	CodeCode Available
Quantifying Cryptocurrency Unpredictability: A Comprehensive Study of Complexity and Forecasting	Feb 13, 2025	Time SeriesTime Series Forecasting	—Unverified
Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting	Feb 13, 2025	Federated LearningPrivacy Preserving	—Unverified
Relational Conformal Prediction for Correlated Time Series	Feb 13, 2025	Conformal PredictionPrediction	—Unverified
Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?	Feb 13, 2025	Time SeriesTime Series Forecasting	—Unverified
Two-stage hybrid models for enhancing forecasting accuracy on heterogeneous time series	Feb 12, 2025	Time SeriesTime Series Forecasting	—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	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