TPS is a patch-level shuffling augmentation for time series forecasting that increases training diversity while preserving local temporal structure, leading to consistent performance gains across multiple models and datasets.
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Temporal Patch Shuffle (TPS): Leveraging Patch-Level Shuffling to Boost Generalization and Robustness in Time Series Forecasting
TPS is a patch-level shuffling augmentation for time series forecasting that increases training diversity while preserving local temporal structure, leading to consistent performance gains across multiple models and datasets.