PRISM用对称卷积减少参数量，高效分类多变量时间序列。
PRISM: Lightweight Multivariate Time-Series Classification through Symmetric Multi-Resolution Convolutional Layers
- 采用对称多分辨率卷积，通道独立处理，降低初期参数量。
- 在多个数据集上性能媲美或超越主流模型，参数量更少。
- 适合资源受限场景，如可穿戴设备上的实时信号分类。
多变量时间序列分类支持可穿戴传感、生物医学监测等应用，需捕捉短期模式与多尺度时序依赖。尽管近期进展显著，但Transformer和CNN模型通常计算开销大、参数多。本文提出PRISM（Per-channel Resolution Informed Symmetric Module），一种轻量级全卷积分类器。早期阶段以通道独立方式应用一组多分辨率对称卷积滤波器，其对称性受经典信号处理中线性相位FIR滤波器启发，有效将初始层可学习参数减半，同时保持完整感受野。在UEA多变量时间序列归档及人体活动识别、睡眠分期、生物信号等特定基准上，PRISM性能匹配或优于现有CNN与Transformer模型，且参数更少、计算成本更低。通过将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高效经济的解决方案。代码与数据见https://github.com/fedezuc/PRISM。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。