用自适应时间窗捕捉多尺度模式，提升时序分类精度
Adaptive Law-Based Transformation (ALT): A Lightweight Feature Representation for Time Series Classification
- 基于可变长度滑动窗口动态提取时序特征
- 在多个数据集上达到当前最优性能，仅需少量超参数
- 方法轻量高效，适合实时性要求高的场景
时序分类（TSC）在金融、医疗和环境监测等领域具有基础性作用。传统方法难以应对时序数据固有的复杂性和多样性。在先前线性规律变换（LLT）工作的基础上，本文提出自适应规律变换（ALT），通过引入可变长度的偏移时间窗，能够捕捉不同长度的区分性模式，从而更有效地处理复杂时序数据。通过将特征映射到线性可分空间，ALT提供了一种快速、鲁棒且透明的解决方案，在仅需少数超参数的情况下实现了最先进的性能。
原文摘要 · Abstract (English)
Time series classification (TSC) is fundamental in numerous domains, including finance, healthcare, and environmental monitoring. However, traditional TSC methods often struggle with the inherent complexity and variability of time series data. Building on our previous work with the linear law-based transformation (LLT) - which improved classification accuracy by transforming the feature space based on key data patterns - we introduce adaptive law-based transformation (ALT). ALT enhances LLT by incorporating variable-length shifted time windows, enabling it to capture distinguishing patterns of various lengths and thereby handle complex time series more effectively. By mapping features into a linearly separable space, ALT provides a fast, robust, and transparent solution that achieves state-of-the-art performance with only a few hyperparameters.
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