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.