HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916332936626176 |
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| author | Keshavarzian, Alireza Valaee, Shahrokh |
| author_facet | Keshavarzian, Alireza Valaee, Shahrokh |
| contents | Time series classification stands as a pivotal and intricate challenge across various domains, including finance, healthcare, and industrial systems. In contemporary research, there has been a notable upsurge in exploring feature extraction through random sampling. Unlike deep convolutional networks, these methods sidestep elaborate training procedures, yet they often necessitate generating a surplus of features to comprehensively encapsulate time series nuances. Consequently, some features may lack relevance to labels or exhibit multi-collinearity with others. In this paper, we propose a novel hierarchical feature selection method aided by ANOVA variance analysis to address this challenge. Through meticulous experimentation, we demonstrate that our method substantially reduces features by over 94% while preserving accuracy -- a significant advancement in the field of time series analysis and feature selection. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_16048 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis Keshavarzian, Alireza Valaee, Shahrokh Machine Learning Information Theory Time series classification stands as a pivotal and intricate challenge across various domains, including finance, healthcare, and industrial systems. In contemporary research, there has been a notable upsurge in exploring feature extraction through random sampling. Unlike deep convolutional networks, these methods sidestep elaborate training procedures, yet they often necessitate generating a surplus of features to comprehensively encapsulate time series nuances. Consequently, some features may lack relevance to labels or exhibit multi-collinearity with others. In this paper, we propose a novel hierarchical feature selection method aided by ANOVA variance analysis to address this challenge. Through meticulous experimentation, we demonstrate that our method substantially reduces features by over 94% while preserving accuracy -- a significant advancement in the field of time series analysis and feature selection. |
| title | HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis |
| topic | Machine Learning Information Theory |
| url | https://arxiv.org/abs/2407.16048 |