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 110 of 1609 papers

TitleStatusHype
The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series ForecastingCode0
Data Augmentation in Time Series Forecasting through Inverted Framework0
Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching0
Foundation models for time series forecasting: Application in conformal prediction0
MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting ModelsCode2
AI-Based Demand Forecasting and Load Balancing for Optimising Energy use in Healthcare Systems: A real case study0
Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and ProjectionCode0
SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs0
A foundation model with multi-variate parallel attention to generate neuronal activityCode1
FAF: A Feature-Adaptive Framework for Few-Shot Time Series Forecasting0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DLinearMSE0.38Unverified
2TEFNMSE0.38Unverified
3MoLE-DLinearMSE0.36Unverified
4FiLMMSE0.36Unverified
5NLinearMSE0.34Unverified
6PatchTST/64MSE0.34Unverified
7MoLE-RLinearMSE0.34Unverified
8PRformerMSE0.33Unverified
9TiDEMSE0.33Unverified
10LTBoost (drop_last=false)MSE0.33Unverified