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 51–100 of 1609 papers

TitleStatusHype
TimeMachine: A Time Series is Worth 4 Mambas for Long-term ForecastingCode3
FilterNet: Harnessing Frequency Filters for Time Series ForecastingCode3
The Rise of Diffusion Models in Time-Series ForecastingCode3
The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple FeaturesCode3
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core FusionCode3
GluonTS: Probabilistic Time Series Models in PythonCode3
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series TransformerCode3
SparseTSF: Modeling Long-term Time Series Forecasting with 1k ParametersCode3
This Time is Different: An Observability Perspective on Time Series Foundation ModelsCode3
Is Mamba Effective for Time Series Forecasting?Code3
PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series ForecastingCode2
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series ForecastingCode2
Proactive Model Adaptation Against Concept Drift for Online Time Series ForecastingCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series ForecastingCode2
Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series ForecastingCode2
PGN: The RNN's New Successor is Effective for Long-Range Time Series ForecastingCode2
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern GeneratorsCode2
Patch-wise Structural Loss for Time Series ForecastingCode2
One Fits All:Power General Time Series Analysis by Pretrained LMCode2
Probabilistic Time Series Forecasting with Implicit Quantile NetworksCode2
Learning Deep Time-index Models for Time Series ForecastingCode2
Non-stationary Diffusion For Probabilistic Time Series ForecastingCode2
N-HiTS: Neural Hierarchical Interpolation for Time Series ForecastingCode2
Non-stationary Transformers: Exploring the Stationarity in Time Series ForecastingCode2
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing FlowsCode2
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel MixingCode2
Multi-Patch Prediction: Adapting LLMs for Time Series Representation LearningCode2
MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting ModelsCode2
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series ForecastingCode2
Deep Learning for Time Series Forecasting: Tutorial and Literature SurveyCode2
PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series ForecastingCode2
ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction HorizonsCode2
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic ForecastingCode2
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and ChallengesCode2
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series ForecastingCode2
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-AdaptCode2
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution ShiftCode2
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series ForecastingCode2
Large language models can be zero-shot anomaly detectors for time series?Code2
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series ForecastingCode2
Model scale versus domain knowledge in statistical forecasting of chaotic systemsCode2
LibCity: An Open Library for Traffic PredictionCode2
MambaTS: Improved Selective State Space Models for Long-term Time Series ForecastingCode2
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series ForecastingCode2
KAN4TSF: Are KAN and KAN-based models Effective for Time Series Forecasting?Code2
ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series ForecastingCode2
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series ForecastingCode2
Auto-Regressive Moving Diffusion Models for Time Series ForecastingCode2
Context is Key: A Benchmark for Forecasting with Essential Textual InformationCode2
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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