PRISM：通过对称多分辨率卷积层实现轻量级多变量时间序列分类
PRISM: Lightweight Multivariate Time-Series Classification through Symmetric Multi-Resolution Convolutional Layers
AI总结：
PRISM通过对称多分辨率卷积层实现轻量级多变量时间序列分类，采用通道无关方式，在早期阶段应用多分辨率对称卷积滤波器，有效减少参数数量并保持完整感受野，优于现有CNN和Transformer模型。
摘要：
多变量时间序列分类支持从可穿戴传感到生物医学监测的应用，需要能够捕捉短期模式和多尺度时间依赖性的模型。尽管近年来取得进展，但Transformer和CNN模型通常计算成本高且依赖大量参数。本文提出PRISM（Per-channel Resolution Informed Symmetric Module），一种轻量全卷积分类器。其在早期阶段应用多分辨率对称卷积滤波器，这种对称性受到经典信号处理中线性相位FIR滤波器的启发，有效将初始层的可学习参数数量减半，同时保持完整感受野。在多样化的UEA多变量时间序列存档以及人类活动识别、睡眠分期和生物医学信号等特定基准上，PRISM在参数数量和计算成本上显著低于现有模型，同时在性能上匹配或优于最先进的CNN和Transformer模型。通过将经典信号处理先验引入现代神经架构，PRISM为多变量时间序列分类提供了有效且计算经济的解决方案。代码和数据可在https://github.com/fedezuc/PRISM获取。
英文摘要：
Multivariate time series classification supports applications from wearable sensing to biomedical monitoring and demands models that can capture both short-term patterns and multi-scale temporal dependencies. Despite recent advances, Transformer and CNN models often remain computationally heavy and rely on many parameters. This work presents PRISM(Per-channel Resolution Informed Symmetric Module), a lightweight fully convolutional classifier. Operating in a channel-independent manner, in its early stage it applies a set of multi-resolution symmetric convolutional filters. This symmetry enforces structural constraints inspired by linear-phase FIR filters from classical signal processing, effectively halving the number of learnable parameters within the initial layers while preserving the full receptive field. Across the diverse UEA multivariate time-series archive as well as specific benchmarks in human activity recognition, sleep staging, and biomedical signals, PRISM matches or outperforms state-of-the-art CNN and Transformer models while using significantly fewer parameters and markedly lower computational cost. By bringing a principled signal processing prior into a modern neural architecture, PRISM offers an effective and computationally economical solution for multivariate time series classification. Code and data are available at https://github.com/fedezuc/PRISM
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
