ALT: A Python Package for Lightweight Feature Representation in Time Series Classification
This provides an efficient tool for time series classification in physics and related domains, though it appears incremental as an enhancement to an existing method.
The authors tackled time series classification by introducing ALT, a Python package that transforms raw data into a linearly separable feature space using adaptive time windows, achieving state-of-the-art performance with minimal computational overhead.
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.