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 301–350 of 1609 papers

TitleStatusHype
Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural NetworksCode1
Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional NetworkCode1
Instance-wise Graph-based Framework for Multivariate Time Series ForecastingCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
Inductive Graph Neural Networks for Spatiotemporal KrigingCode1
Informer: Beyond Efficient Transformer for Long Sequence Time-Series ForecastingCode1
Integrating LSTMs and GNNs for COVID-19 ForecastingCode1
Combating Distribution Shift for Accurate Time Series Forecasting via HypernetworksCode1
How Much Can Time-related Features Enhance Time Series Forecasting?Code1
Copula Conformal Prediction for Multi-step Time Series ForecastingCode1
ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical ImagesCode1
Hierarchical Classification Auxiliary Network for Time Series ForecastingCode1
Improved Online Conformal Prediction via Strongly Adaptive Online LearningCode1
Large Language Models for Financial Aid in Financial Time-series ForecastingCode1
Graph Neural Networks for Improved El Niño ForecastingCode1
Graph Neural Controlled Differential Equations for Traffic ForecastingCode1
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural NetworksCode1
Counterfactual Explanations for Time Series ForecastingCode1
Conformal Time-series ForecastingCode1
Graph-based Time Series Clustering for End-to-End Hierarchical ForecastingCode1
Handling Concept Drift in Global Time Series ForecastingCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
CondTSF: One-line Plugin of Dataset Condensation for Time Series ForecastingCode1
CrossLinear: Plug-and-Play Cross-Correlation Embedding for Time Series Forecasting with Exogenous VariablesCode1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality ModelingCode1
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series ForecastingCode1
Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic ForecastingCode1
HDT: Hierarchical Discrete Transformer for Multivariate Time Series ForecastingCode1
AtsPy: Automated Time Series Forecasting in PythonCode1
LightCTS: A Lightweight Framework for Correlated Time Series ForecastingCode1
GBT: Two-stage transformer framework for non-stationary time series forecastingCode1
Gateformer: Advancing Multivariate Time Series Forecasting through Temporal and Variate-Wise Attention with Gated RepresentationsCode1
Coherent Probabilistic Aggregate Queries on Long-horizon ForecastsCode1
Long-Range Transformers for Dynamic Spatiotemporal ForecastingCode1
Generative Pretrained Hierarchical Transformer for Time Series ForecastingCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
FreEformer: Frequency Enhanced Transformer for Multivariate Time Series ForecastingCode1
CARD: Channel Aligned Robust Blend Transformer for Time Series ForecastingCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
Mamba time series forecasting with uncertainty quantificationCode1
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal ModelingCode1
MemDA: Forecasting Urban Time Series with Memory-based Drift AdaptationCode1
FrAug: Frequency Domain Augmentation for Time Series ForecastingCode1
FreqMoE: Enhancing Time Series Forecasting through Frequency Decomposition Mixture of ExpertsCode1
FlexTSF: A Universal Forecasting Model for Time Series with Variable RegularitiesCode1
Deep Adaptive Input Normalization for Time Series ForecastingCode1
Multi-modal learning for geospatial vegetation forecastingCode1
First De-Trend then Attend: Rethinking Attention for Time-Series ForecastingCode1
Deep Autoregressive Models with Spectral AttentionCode1
Forecasting with Deep LearningCode1
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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