Time-Series Forecasting Augmentation: Frequency, Decomposition, and Patch Methods Compared

The article examines the challenges of augmenting time-series forecasting, reviews mainstream techniques—including frequency-domain, decomposition, and patch-based methods—and demonstrates through extensive experiments that Temporal Patch Shuffle (TPS) consistently achieves superior performance across long-term, short-term, and classification tasks.

Temporal Patch Shuffledata augmentationfrequency domain

0 likes · 20 min read

Time-Series Forecasting Augmentation: Frequency, Decomposition, and Patch Methods Compared

Data Party THU

Apr 30, 2026 · Artificial Intelligence

Time Series Forecasting Augmentation: Frequency, Decomposition, and Patch Techniques

This article reviews why classic classification augmentations fail for forecasting, introduces the essential data‑label consistency requirement, and systematically categorizes effective time‑series augmentation methods—including frequency‑domain (RobustTAD, FreqMask, FreqMix), decomposition (STAug), and patch‑based approaches (WaveMask, WaveMix, Dominant Shuffle, Temporal Patch Shuffle)—backed by extensive experiments on long‑term, short‑term, and classification tasks.

Temporal Patch Shuffledata augmentationfrequency domain

0 likes · 20 min read

Time Series Forecasting Augmentation: Frequency, Decomposition, and Patch Techniques

DeepHub IMBA

Apr 22, 2026 · Artificial Intelligence

A Survey of Time Series Forecasting Augmentation: Frequency Domain, Decomposition, and Patch Methods

The article reviews why classic classification augmentations fail for forecasting, outlines a taxonomy of effective time‑series augmentation techniques—including frequency‑domain, decomposition, and patch‑based methods—details the Temporal Patch Shuffle (TPS) pipeline, and presents extensive experiments showing TPS achieves state‑of‑the‑art improvements across long‑term, short‑term, and classification tasks.

Machine LearningTemporal Patch ShuffleTime-series

0 likes · 17 min read

A Survey of Time Series Forecasting Augmentation: Frequency Domain, Decomposition, and Patch Methods