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 51–75 of 1609 papers

TitleStatusHype
Probabilistic Forecasting with Temporal Convolutional Neural NetworkCode3
Lag-Llama: Towards Foundation Models for Probabilistic Time Series ForecastingCode3
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core FusionCode3
OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed DomainCode3
FilterNet: Harnessing Frequency Filters for Time Series ForecastingCode3
MMFNet: Multi-Scale Frequency Masking Neural Network for Multivariate Time Series ForecastingCode3
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series ForecastingCode3
AutoTimes: Autoregressive Time Series Forecasters via Large Language ModelsCode3
SparseTSF: Modeling Long-term Time Series Forecasting with 1k ParametersCode3
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image RecognitionCode3
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series ForecastingCode2
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and ChallengesCode2
MambaTS: Improved Selective State Space Models for Long-term Time Series ForecastingCode2
Model scale versus domain knowledge in statistical forecasting of chaotic systemsCode2
Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series ForecastingCode2
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution ShiftCode2
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern GeneratorsCode2
Large language models can be zero-shot anomaly detectors for time series?Code2
LibCity: An Open Library for Traffic PredictionCode2
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning ProcessCode2
KAN4TSF: Are KAN and KAN-based models Effective for Time Series Forecasting?Code2
HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?Code2
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series ForecastingCode2
ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series ForecastingCode2
A Temporal Kolmogorov-Arnold Transformer 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