HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis
<|reference_start|>HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis: 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.<|reference_end|>
@article{keshavarzian2024hiervar:,
    title={HIERVAR: A Hierarchical Feature Selection Method for Time Series
  Analysis},
    author={Alireza Keshavarzian, Shahrokh Valaee},
    journal={arXiv preprint arXiv:2407.16048},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16048},
    primaryClass={cs.LG cs.IT math.IT}
}