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...