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.

翻译：时间序列分类是金融、医疗和工业系统等多个领域中的关键且复杂的挑战。在当代研究中，通过随机采样进行特征提取的方法显著增多。与深度卷积网络不同，这些方法避开了复杂的训练过程，但通常需要生成大量特征以全面捕捉时间序列的细微差别。因此，某些特征可能与标签无关，或与其他特征存在多重共线性。本文提出了一种新颖的层次化特征选择方法，借助方差分析（ANOVA）来解决这一挑战。通过细致的实验，我们证明该方法在保持准确性的同时，将特征数量大幅减少了94%以上——这是时间序列分析与特征选择领域的一项重要进展。