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.

翻译：时间序列分类(TSC)在金融、医疗保健和环境监测等诸多领域具有基础性地位。然而，传统TSC方法往往难以应对时间序列数据固有的复杂性和多变性。基于我们先前在线性规律变换(LLT)方面的工作——该方法通过基于关键数据模式变换特征空间来提高分类准确率——我们提出了自适应规律变换(ALT)。ALT通过引入可变长度的滑动时间窗口来增强LLT，使其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间序列。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，仅需少量超参数即可实现最先进的性能。