Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147\,000 time series) and using a filtered version of the hctsa feature library (4791 features), we introduce a generically useful set of 22 CAnonical Time-series CHaracteristics, catch22. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7\%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties.

通过系统比较卓越时间序列特征库中的时间序列的适当特征化表示形式，可以捕捉时间序列的动态特性，为时间序列应用程序提供高效的聚类和分类能力。我们通过减少时间序列特征的数量，从和特性库中的原始数量的 4791 种特征中提取出一个名为 catch22 的定义良好、准确有效并不冗余的时间序列特征集，这个集合只包含有 22 个时间序列特征。这种降维数据的方法可以在保持准确性的情况下大大减轻计算负担，并通过让科学、工业、金融和医学应用程序可以使用一种可解释性的时间序列特征语言，从而促进其应用。

catch22: 规范化时间序列特征