ALT: A Python Package for Lightweight Feature Representation in Time Series Classification
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915776726827008 |
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| author | Halmos, Balázs P. Hajós, Balázs Molnár, Vince Á. Kurbucz, Marcell T. Jakovác, Antal |
| author_facet | Halmos, Balázs P. Hajós, Balázs Molnár, Vince Á. Kurbucz, Marcell T. Jakovác, Antal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12841 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ALT: A Python Package for Lightweight Feature Representation in Time Series Classification Halmos, Balázs P. Hajós, Balázs Molnár, Vince Á. Kurbucz, Marcell T. Jakovác, Antal Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Mathematical Software 62M10, 62H30, 68T05, 68T10 I.5.1; I.2.6; G.3; D.2.13 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. |
| title | ALT: A Python Package for Lightweight Feature Representation in Time Series Classification |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Mathematical Software 62M10, 62H30, 68T05, 68T10 I.5.1; I.2.6; G.3; D.2.13 |
| url | https://arxiv.org/abs/2504.12841 |