HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis

• URL: http://arxiv.org/abs/2407.16048v1
• Date: Mon, 22 Jul 2024 20:55:13 GMT
• Title: HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis
• Authors: Alireza Keshavarzian, Shahrokh Valaee,
• Abstract summary: Time series classification stands as a pivotal and intricate challenge across various domains. We propose a novel hierarchical feature selection method aided by ANOVA variance analysis. Our method substantially reduces features by over 94% while preserving accuracy.
• Score: 22.285570102169356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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