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

Kuaishou Tech

Dec 18, 2025 · Artificial Intelligence

How SSR Turns Multimodal Recommendation into an Interpretable Frequency‑Domain Reasoning Problem

The paper introduces SSR, a novel multimodal recommendation framework that leverages graph Fourier transforms, energy‑balanced frequency bands, structured regularization, and low‑rank tensor decomposition to replace black‑box fusion with explainable, adaptive reasoning, achieving state‑of‑the‑art results on Amazon datasets and strong cold‑start performance.

cold startcontrastive learningfrequency domain

0 likes · 15 min read

How SSR Turns Multimodal Recommendation into an Interpretable Frequency‑Domain Reasoning Problem

Data Party THU

Nov 22, 2025 · Artificial Intelligence

How Frequency‑Refined Augmentation Boosts Contrastive Learning for Time‑Series Classification

FreRA introduces a lightweight, plug‑in frequency‑refined augmentation that adaptively refines spectral components to preserve global semantics while injecting variance, dramatically improving contrastive learning performance on time‑series classification, anomaly detection, and transfer learning across multiple benchmark datasets.

Time-seriescontrastive learningdata augmentation

0 likes · 13 min read

How Frequency‑Refined Augmentation Boosts Contrastive Learning for Time‑Series Classification

Alibaba Cloud Developer

Mar 10, 2020 · Artificial Intelligence

Can Frequency‑Domain Learning Boost Image Inference Efficiency?

This article presents a system‑level approach that performs deep‑learning inference directly on JPEG frequency components, uses a gating mechanism to select important DCT coefficients, and demonstrates higher accuracy with far lower bandwidth for image classification and instance segmentation tasks.

Bandwidth Reductioncomputer visiondeep learning

0 likes · 22 min read

Can Frequency‑Domain Learning Boost Image Inference Efficiency?