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 451–500 of 1609 papers

TitleStatusHype
Graph Neural Controlled Differential Equations for Traffic ForecastingCode1
First De-Trend then Attend: Rethinking Attention for Time-Series ForecastingCode1
TSMixer: An All-MLP Architecture for Time Series ForecastingCode1
Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal NarrativeCode1
Modeling Long- and Short-Term Temporal Patterns with Deep Neural NetworksCode1
CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction—0
A novel stochastic model based on echo state networks for hydrological time series forecasting—0
Forecasting Cardiology Admissions from Catheterization Laboratory—0
CAPE: Covariate-Adjusted Pre-Training for Epidemic Time Series Forecasting—0
A Forecaster's Review of Judea Pearl's Causality: Models, Reasoning and Inference, Second Edition, 2009—0
AdaPRL: Adaptive Pairwise Regression Learning with Uncertainty Estimation for Universal Regression Tasks—0
Can time series forecasting be automated? A benchmark and analysis—0
A novel method of fuzzy time series forecasting based on interval index number and membership value using support vector machine—0
A federated large language model for long-term time series forecasting—0
Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modeling—0
Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting—0
Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs—0
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts—0
Can LLMs Serve As Time Series Anomaly Detectors?—0
Can Competition Enhance the Proficiency of Agents Powered by Large Language Models in the Realm of News-driven Time Series Forecasting?—0
A Novel Hybrid Approach Using an Attention-Based Transformer + GRU Model for Predicting Cryptocurrency Prices—0
Enhancing Financial Time-Series Forecasting with Retrieval-Augmented Large Language Models—0
Enhancing Financial Data Visualization for Investment Decision-Making—0
Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference—0
A Novel Distributed PV Power Forecasting Approach Based on Time-LLM—0
Byte Pair Encoding for Efficient Time Series Forecasting—0
Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction—0
A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information—0
Enhanced Prediction Model for Time Series Characterized by GARCH via Interval Type-2 Fuzzy Inference System—0
Enforcing Interpretability in Time Series Transformers: A Concept Bottleneck Framework—0
Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models—0
Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods—0
ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks—0
Forecasting and Analyzing the Military Expenditure of India Using Box-Jenkins ARIMA Model—0
Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding—0
CAIFormer: A Causal Informed Transformer for Multivariate Time Series Forecasting—0
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts—0
Energy consumption forecasting using a stacked nonparametric Bayesian approach—0
End-to-End Probabilistic Framework for Learning with Hard Constraints—0
Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation—0
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization—0
Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment—0
Enhancing Multi-Step Brent Oil Price Forecasting with Ensemble Multi-Scenario Bi-GRU Networks—0
Multistep Brent Oil Price Forecasting with a Multi-Aspect Meta-heuristic Optimization and Ensemble Deep Learning Model—0
Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon—0
Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Research—0
Enhancing Project Performance Forecasting using Machine Learning Techniques—0
Can Local Representation Alignment RNNs Solve Temporal Tasks?—0
Encoding Temporal Statistical-space Priors via Augmented Representation—0
Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching—0
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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