PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910001399857152 |
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| author | Spinnato, Francesco Landi, Cristiano |
| author_facet | Spinnato, Francesco Landi, Cristiano |
| contents | Irregular temporal data, characterized by varying recording frequencies, differing observation durations, and missing values, presents significant challenges across fields like mobility, healthcare, and environmental science. Existing research communities often overlook or address these challenges in isolation, leading to fragmented tools and methods. To bridge this gap, we introduce a unified framework, and the first standardized dataset repository for irregular time series classification, built on a common array format to enhance interoperability. This repository comprises 34 datasets on which we benchmark 12 classifier models from diverse domains and communities. This work aims to centralize research efforts and enable a more robust evaluation of irregular temporal data analysis methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06047 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks Spinnato, Francesco Landi, Cristiano Machine Learning Artificial Intelligence Irregular temporal data, characterized by varying recording frequencies, differing observation durations, and missing values, presents significant challenges across fields like mobility, healthcare, and environmental science. Existing research communities often overlook or address these challenges in isolation, leading to fragmented tools and methods. To bridge this gap, we introduce a unified framework, and the first standardized dataset repository for irregular time series classification, built on a common array format to enhance interoperability. This repository comprises 34 datasets on which we benchmark 12 classifier models from diverse domains and communities. This work aims to centralize research efforts and enable a more robust evaluation of irregular temporal data analysis methods. |
| title | PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.06047 |