catch22: CAnonical Time-series CHaracteristics Selected through highly comparative time-series analysis
catch22: CAnonical Time-series CHaracteristics Selected through highly comparative time-series analysis
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DOI：
10.1007/s10618-019-00647-x
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发表时间：
2019-11-01
影响因子：
4.8
通讯作者：
Jones, Nick S.
中科院分区：
文献类型：
--
作者：
Lubba, Carl H.;Sethi, Sarab S.;Jones, Nick S.
将时间序列的动态特性简洁地捕获为可解释的特征向量可以为跨科学和工业的时间序列应用提供有效的聚类和分类。为给定的应用程序选择一个合适的基于特征的时间序列表示可以通过系统地比较一个综合的时间序列特征库来实现，比如hctsa工具箱中的那些。然而，这种方法在计算上是昂贵的，并涉及评估许多类似的功能，限制了广泛采用的基于特征的表示的时间序列的现实世界的应用。在这项工作中，我们引入了一种方法来推断小的时间序列特征集，（i）在给定的时间序列问题集合中表现出很强的分类性能，（ii）冗余度最小。将我们的方法应用于一组93个时间序列分类数据集（包含超过147，000个时间序列），并使用hctsa特征库（4791个特征）的过滤版本，我们引入了一组22个CAnonical时间序列特征，catch22，适合于时间序列数据挖掘任务中通常遇到的动态。这种维数从4791降到22，与计算时间减少约1000倍和时间序列长度接近线性缩放相关，尽管分类准确度平均降低仅7%。catch 22从时间序列的属性（包括线性和非线性自相关、连续差异、值分布和离群值以及波动缩放属性）方面捕获了时间序列的多样化且可解释的特征。我们提供了一个有效的实现catch22，可从许多编程环境，促进基于功能的时间序列分析，科学，工业，金融和医疗应用程序使用一个共同的语言的可解释的时间序列属性。
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 set of 22 CAnonical Time-series CHaracteristics, catch22, tailored to the dynamics typically encountered in time-series data-mining tasks. 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.