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 351–400 of 1609 papers

TitleStatusHype
Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter ViewCode1
GBT: Two-stage transformer framework for non-stationary time series forecastingCode1
Parameter Efficient Deep Probabilistic ForecastingCode1
Handling Concept Drift in Global Time Series ForecastingCode1
Non-Stationary Time Series Forecasting Based on Fourier Analysis and Cross Attention MechanismCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix FactorizationCode1
PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep LearningCode1
Prediction of the Position of External Markers Using a Recurrent Neural Network Trained With Unbiased Online Recurrent Optimization for Safe Lung Cancer RadiotherapyCode1
Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLMsCode1
Enhancing Battery Storage Energy Arbitrage with Deep Reinforcement Learning and Time-Series ForecastingCode1
NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series ForecastingCode1
A Multi-view Multi-task Learning Framework for Multi-variate Time Series ForecastingCode1
Neural Conformal Control for Time Series ForecastingCode1
Graph Neural Networks for Improved El Niño ForecastingCode1
Embedded feature selection in LSTM networks with multi-objective evolutionary ensemble learning for time series forecastingCode1
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEsCode1
Time series forecasting with Gaussian Processes needs priorsCode1
HDT: Hierarchical Discrete Transformer for Multivariate Time Series ForecastingCode1
Hierarchical Classification Auxiliary Network for Time Series ForecastingCode1
Ensemble Conformalized Quantile Regression for Probabilistic Time Series ForecastingCode1
SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time SeriesCode1
How Much Can Time-related Features Enhance Time Series Forecasting?Code1
Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic DataCode1
Inductive Graph Neural Networks for Spatiotemporal KrigingCode1
Improved Online Conformal Prediction via Strongly Adaptive Online LearningCode1
ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical ImagesCode1
DEFM: Delay E mbedding based Forecast Machine for Time Series Forecasting by Spatiotemporal Information TransformationCode1
Deep Switching State Space Model (DS^3M) for Nonlinear Time Series Forecasting with Regime SwitchingCode1
Multivariate Probabilistic Time Series Forecasting with Correlated ErrorsCode1
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction taskCode1
CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial CorrelationsCode1
Multi-scale Transformer Pyramid Networks for Multivariate Time Series ForecastingCode1
Multivariate Time Series Forecasting with Transfer Entropy GraphCode1
CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series ForecastingCode1
Dynamic Graph Learning-Neural Network for Multivariate Time Series ModelingCode1
Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural NetworksCode1
Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series ForecastingCode1
Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional NetworkCode1
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series ForecastingCode1
Multi-Variate Time Series Forecasting on Variable SubsetsCode1
K^2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series ForecastingCode1
MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question AnsweringCode1
D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series ForecastingCode1
Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal NarrativeCode1
Superiority of Simplicity: A Lightweight Model for Network Device Workload PredictionCode1
Do We Really Need Deep Learning Models for Time Series Forecasting?Code1
MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series ForecastingCode1
Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolutionCode1
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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