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

• Published in: Mathematics (Basel) Vol. 9; no. 23; p. 3051
• Main Authors: López-Oriona, Ángel, Vilar, José A.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021
• Subjects: Algorithms; Classification; Datasets; Deep learning; Discrete Wavelet Transform; Discriminant analysis; ECG signals; Electrocardiography; Feature extraction; Feature selection; Internet of Things; Mathematics; Multivariate analysis; multivariate time series; Neural networks; quantile analysis; random forest; Support vector machines; Time series; wavelet analysis; Wavelet transforms

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...