ALT用自适应时间窗转换时序数据，提升分类精度且计算开销小。
ALT: A Python Package for Lightweight Feature Representation in Time Series Classification
- 通过可变长度时移窗口将原始时序转为线性可分特征空间。
- 在真实世界数据集上表现优于现有方法，计算开销极低。
- 适合需要高效、可解释时序分类的物理等领域研究者使用。
我们介绍ALT，一个开源的Python工具包，用于高效且准确的时序分类（TSC）。该工具包实现了自适应定律变换（ALT）算法，利用可变长度的时移时间窗，将原始时序数据转换为线性可分的特征空间。相比其前身线性定律变换（LLT），该方法能有效捕捉不同时间尺度的模式。软件设计注重可扩展性、可解释性和易用性，在真实世界数据集上的广泛基准测试表明，ALT在物理学及相关领域的多样化时序分类任务中具有显著实用性，实现前沿性能的同时保持极低的计算开销。
原文摘要 · Abstract (English)
We introduce ALT, an open-source Python package created for efficient and accurate time series classification (TSC). The package implements the adaptive law-based transformation (ALT) algorithm, which transforms raw time series data into a linearly separable feature space using variable-length shifted time windows. This adaptive approach enhances its predecessor, the linear law-based transformation (LLT), by effectively capturing patterns of varying temporal scales. The software is implemented for scalability, interpretability, and ease of use, achieving state-of-the-art performance with minimal computational overhead. Extensive benchmarking on real-world datasets demonstrates the utility of ALT for diverse TSC tasks in physics and related domains.
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