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arXiv · 2407.16048

HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis

Alireza Keshavarzian·Shahrokh Valaee
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Abstract

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.

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Alireza Keshavarzian, Shahrokh Valaee. 2024-07-22. HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis. https://arxiv.org/abs/2407.16048

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