F4: An All-Purpose Tool for Multivariate Time Series Classification

We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying generating processes. This goal has barely been addressed in the literature. F4 consists of two steps. First, a set of features based on the quantile cross-spectral density and the maximum overlap discrete wavelet transform are extracted from each series. Second, a random forest is fed with the extracted features. An extensive simulation study shows that F4 outperforms some powerful classifiers in a wide variety of situations, including stationary and nonstationary series. The proposed method is also capable of successfully discriminating between electrocardiogram (ECG) signals of healthy subjects and those with myocardial infarction condition. Additionally, despite lacking shape-based information, F4 attains state-of-the-art results in some datasets of the University of East Anglia (UEA) multivariate time series classification archive.

Automated summary

Fast Forest of Flexible Features (F4) is a novel approach for classifying multivariate time series, aimed at discriminating between generating processes; F4 consists of feature extraction and random forest classification steps; the method outperforms other classifiers in a wide range of scenarios and shows promising results in discriminating between different health conditions.