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

نویسندگان

چکیده

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 two steps. First, set features based on quantile cross-spectral density and maximum overlap discrete wavelet transform are extracted from each series. Second, random forest fed with features. An extensive simulation study shows that outperforms some powerful classifiers wide variety situations, including stationary nonstationary The proposed method also capable successfully discriminating electrocardiogram (ECG) signals healthy subjects those myocardial infarction condition. Additionally, despite lacking shape-based information, attains state-of-the-art results datasets University East Anglia (UEA) series classification archive.

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ژورنال

عنوان ژورنال: Mathematics

سال: 2021

ISSN: ['2227-7390']

DOI: https://doi.org/10.3390/math9233051