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 601650 of 1609 papers

TitleStatusHype
Multiple-Resolution Tokenization for Time Series Forecasting with an Application to PricingCode0
Fi^2VTS: Time Series Forecasting Via Capturing Intra- and Inter-Variable Variations in the Frequency DomainCode0
Alternative Telescopic Displacement: An Efficient Multimodal Alignment MethodCode0
Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time InterventionsCode0
Multiple model estimation under perspective of random-fuzzy dual interpretation of unknown uncertaintyCode0
Correlated daily time series and forecasting in the M4 competitionCode0
Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation modelCode0
MTS-UNMixers: Multivariate Time Series Forecasting via Channel-Time Dual UnmixingCode0
Fourier-Mixed Window Attention: Accelerating Informer for Long Sequence Time-Series ForecastingCode0
MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series ForecastingCode0
Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with TransformersCode0
Model selection in reconciling hierarchical time seriesCode0
Modeling the Dynamics of Growth in Master-Planned CommunitiesCode0
FocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time SeriesCode0
Model Compression for Dynamic Forecast CombinationCode0
Forecasting with Deep Learning: Beyond Average of Average of Average PerformanceCode0
MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series ForecastingCode0
Minimal Time Series TransformerCode0
Forecasting time series with constraintsCode0
Accurate Uncertainties for Deep Learning Using Calibrated RegressionCode0
Forecasting Precipitable Water Vapor Using LSTMsCode0
Model Selection for Time Series Forecasting: Empirical Analysis of Different EstimatorsCode0
MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer RechargeCode0
MAGMA: Inference and Prediction with Multi-Task Gaussian ProcessesCode0
Machine Learning vs Statistical Methods for Time Series Forecasting: Size MattersCode0
Conformal time series decomposition with component-wise exchangeabilityCode0
Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variablesCode0
FPN-fusion: Enhanced Linear Complexity Time Series Forecasting ModelCode0
Conditional Time Series Forecasting with Convolutional Neural NetworksCode0
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering ApproachCode0
Forecasting Algorithms for Causal Inference with Panel DataCode0
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal DynamicsCode0
Conditional Temporal Neural Processes with Covariance LossCode0
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling lawCode0
A hybrid method of Exponential Smoothing and Recurrent Neural Networks for time series forecastingCode0
LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series ForecastingCode0
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methodsCode0
Flow-based Spatio-Temporal Structured Prediction of Motion DynamicsCode0
Flipped Classroom: Effective Teaching for Time Series ForecastingCode0
Asset Price Forecasting using Recurrent Neural NetworksCode0
Linear Transformers as VAR Models: Aligning Autoregressive Attention Mechanisms with Autoregressive ForecastingCode0
Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series ForecastingCode0
Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen PredictionCode0
Less is more: Embracing sparsity and interpolation with Esiformer for time series forecastingCode0
Leveraging External Factors in Household-Level Electrical Consumption Forecasting using HypernetworksCode0
Meta-learning and Data Augmentation for Stress Testing Forecasting ModelsCode0
Multivariate de Bruijn Graphs: A Symbolic Graph Framework for Time Series ForecastingCode0
Functional Latent Dynamics for Irregularly Sampled Time Series ForecastingCode0
Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit ForecastingCode0
Fine-Tuning a Time Series Foundation Model with Wasserstein LossCode0
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Benchmark Results

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