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.
@article{arxiv.2504.12841,
title = {ALT: A Python Package for Lightweight Feature Representation in Time Series Classification},
author = {Balázs P. Halmos and Balázs Hajós and Vince Á. Molnár and Marcell T. Kurbucz and Antal Jakovác},
journal= {arXiv preprint arXiv:2504.12841},
year = {2026}
}