altx: a python package for adaptive law-based transformation in time series classification

Abstract

We introduce altx, an open-source python package for computationally lightweight and transparent time series classification pipelines. The altx package implements the adaptive law-based transformation, a multiscale feature extraction method that maps raw time series to compact tabular feature vectors by pooling class-labeled law responses across windows and scales. The approach extends the linear law-based transformation with a multiscale shifted-window schedule while preserving transparency. The package provides a GPU-capable PyTorch implementation with an estimator-style interface, enabling straightforward integration into modern machine-learning workflows and interoperability with common scientific Python toolkits. We include illustrative examples and summarize representative benchmark results reported in our companion methodological paper.