arXiv AI
Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields
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Hugging Face Trending Papers
Sep 24
Neuralized Multi-Wavelet Decomposition for Time Series Classification and Forecasting
The paper introduces m-WCN, an end‑to‑end deep learning framework that neuralizes multi‑wavelet decomposition to jointly extract temporal patterns and frequency components from time series. It enforces orthogonality constraints to produce interpretable multi‑resolution representations, and builds two task‑specific architectures—TFBC for classification and FTB for forecasting—on top of this foundation. Experiments on 64 UCR datasets and seven forecasting benchmarks show that TFBC and FTB outperform baseline models, achieving average improvements of about 20% in both classification and forecasting tasks.