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Customized Dynamic Filter Augmentation
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AI
DOI：10.1109/LSP.2026.3652120.png)
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Abstract
En 中文
Customized dynamic filter augmentation (CDFA) presents a novel data augmentation technique for time-series forecasting, adapting convolutional principles from signal processing to emphasize historical patterns through localized correlations and amplitude adjustments. Built upon convolutional filters, local correlations between paired random variables, and statistical forecasting functions from compact data learning, CDFA generates plausible subsequences while preserving original data characteristics. Empirical evaluations on real-world datasets, including stock prices for Apple, Google, AMD, and oil, demonstrate superior root mean square error (RMSE) reductions, with CDFA achieving 81% to 82% improvements over baselines like statistical forecasting from CDL and customized convolutional filters. This approach enhances model efficiency for large-scale sequences, outperforming traditional linear models in capturing shared patterns across diverse applications.
Keywords:
Customized dynamic filter
signal processing
time-series
convolutional filter
compact data learning
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3.9
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