  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.
