2025/04/17 by Halmos, Balázs P., Hajós, Balázs, Molnár, Vince Á. +2
#62H30 #62M10 #68T05 #68T10 #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #D.2.13 #FOS: Computer and information sciences #G.3 #I.2.6 #I.5.1 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematical Software (cs.MS)
paper · doi:10.48550/arxiv.2504.12841
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.