SOTAVerified

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

Showing 101–150 of 1609 papers

TitleStatusHype
Patch-wise Structural Loss for Time Series ForecastingCode2
An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock ForecastingCode2
Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge DistillationCode2
PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series ForecastingCode2
Non-stationary Diffusion For Probabilistic Time Series ForecastingCode2
Efficient and Effective Time-Series Forecasting with Spiking Neural NetworksCode2
Non-stationary Transformers: Exploring the Stationarity in Time Series ForecastingCode2
TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality AlignmentCode2
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series ForecastingCode2
TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series ForecastingCode2
ETSformer: Exponential Smoothing Transformers for Time-series ForecastingCode2
PGN: The RNN's New Successor is Effective for Long-Range Time Series ForecastingCode2
RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series TasksCode2
Multi-Patch Prediction: Adapting LLMs for Time Series Representation LearningCode2
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel MixingCode2
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing FlowsCode2
MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting ModelsCode2
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic ForecastingCode2
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series ForecastingCode2
Minusformer: Improving Time Series Forecasting by Progressively Learning ResidualsCode2
Adaptive Multi-Scale Decomposition Framework for Time Series ForecastingCode2
MambaTS: Improved Selective State Space Models for Long-term Time Series ForecastingCode2
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution ShiftCode2
Model scale versus domain knowledge in statistical forecasting of chaotic systemsCode2
LibCity: An Open Library for Traffic PredictionCode2
FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series ForecastingCode2
Deep Learning for Time Series Forecasting: Tutorial and Literature SurveyCode2
KAN4TSF: Are KAN and KAN-based models Effective for Time Series Forecasting?Code2
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series ForecastingCode2
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series ForecastingCode2
HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?Code2
Are Self-Attentions Effective for Time Series Forecasting?Code2
Large Language Models Are Zero-Shot Time Series ForecastersCode2
Generative Time Series Forecasting with Diffusion, Denoise, and DisentanglementCode2
ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series ForecastingCode2
From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with ReflectionCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series ForecastingCode2
GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable MissingCode2
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly DetectionCode2
A Temporal Kolmogorov-Arnold Transformer for Time Series ForecastingCode2
Frequency Adaptive Normalization For Non-stationary Time Series ForecastingCode2
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph PerspectiveCode2
Auto-Regressive Moving Diffusion Models for Time Series ForecastingCode2
Fredformer: Frequency Debiased Transformer for Time Series ForecastingCode2
Frequency-domain MLPs are More Effective Learners in Time Series ForecastingCode2
Large language models can be zero-shot anomaly detectors for time series?Code2
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and ChallengesCode2
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning ProcessCode2
N-HiTS: Neural Hierarchical Interpolation for Time Series ForecastingCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InformerMSE0.88—Unverified
2QuerySelectorMSE0.85—Unverified
3TransformerMSE0.83—Unverified
4AarenMSE0.65—Unverified
5RPMixerMSE0.52—Unverified
6ATFNetMSE0.51—Unverified
7MOIRAILargeMSE0.51—Unverified
8AutoformerMSE0.51—Unverified
9SCINetMSE0.5—Unverified
10S-MambaMSE0.49—Unverified
#ModelMetricClaimedVerifiedStatus
1QuerySelectorMSE1.12—Unverified
2TransformerMSE1.11—Unverified
3InformerMSE0.94—Unverified
4GLinearMSE0.59—Unverified
5SCINetMSE0.54—Unverified
6MoLE-DLinearMSE0.51—Unverified
7PRformerMSE0.49—Unverified
8TEFNMSE0.48—Unverified
9DLinearMSE0.47—Unverified
10FiLMMSE0.47—Unverified
#ModelMetricClaimedVerifiedStatus
1TransformerMSE2.66—Unverified
2QuerySelectorMSE2.32—Unverified
3InformerMSE1.67—Unverified
4DLinearMSE0.45—Unverified
5TEFNMSE0.42—Unverified
6MoLE-DLinearMSE0.42—Unverified
7FiLMMSE0.38—Unverified
8MoLE-RLinearMSE0.37—Unverified
9SCINetMSE0.37—Unverified
10PRformerMSE0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1TransformerMSE3.18—Unverified
2QuerySelectorMSE3.07—Unverified
3InformerMSE2.34—Unverified
4MoLE-DLinearMSE0.61—Unverified
5DLinearMSE0.61—Unverified
6SCINetMSE0.48—Unverified
7FiLMMSE0.44—Unverified
8TEFNMSE0.43—Unverified
9TiDEMSE0.42—Unverified
10MoLE-RLinearMSE0.41—Unverified
#ModelMetricClaimedVerifiedStatus
1MoLE-DLinearMSE0.45—Unverified
2TEFNMSE0.43—Unverified
3FiLMMSE0.41—Unverified
4PatchTST/64MSE0.41—Unverified
5TiDEMSE0.41—Unverified
6NLinearMSE0.41—Unverified
7DiPE-LinearMSE0.41—Unverified
8DLinearMSE0.41—Unverified
9RLinearMSE0.4—Unverified
10MoLE-RLinearMSE0.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DLinearMSE0.38—Unverified
2TEFNMSE0.38—Unverified
3MoLE-DLinearMSE0.36—Unverified
4FiLMMSE0.36—Unverified
5NLinearMSE0.34—Unverified
6PatchTST/64MSE0.34—Unverified
7MoLE-RLinearMSE0.34—Unverified
8TiDEMSE0.33—Unverified
9PRformerMSE0.33—Unverified
10LTBoost (drop_last=false)MSE0.33—Unverified
#ModelMetricClaimedVerifiedStatus
1DLinearMSE0.29—Unverified
2TEFNMSE0.29—Unverified
3MoLE-DLinearMSE0.29—Unverified
4FiLMMSE0.28—Unverified
5NLinearMSE0.28—Unverified
6TSMixerMSE0.28—Unverified
7DiPE-LinearMSE0.28—Unverified
8PatchTST/64MSE0.27—Unverified
9MoLE-RLinearMSE0.27—Unverified
10TiDEMSE0.27—Unverified
#ModelMetricClaimedVerifiedStatus
1TEFNMSE0.38—Unverified
2MoLE-DLinearMSE0.38—Unverified
3TiDEMSE0.38—Unverified
4MoLE-RLinearMSE0.38—Unverified
5FiLMMSE0.37—Unverified
6PatchTST/64MSE0.37—Unverified
7DiPE-LinearMSE0.37—Unverified
8TSMixerMSE0.37—Unverified
9RLinearMSE0.37—Unverified
10TTMMSE0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1TEFNMSE0.23—Unverified
2DLinearMSE0.22—Unverified