Cristina Masoller

Evaluating the classification performance and data requirements of Catch22 features on noisy and short synthetic time series

Abstract This study evaluates the performance, data requirements, and computational efficiency of the feature extraction tool Catch22 for time-series classification. Using synthetic sinusoidal signals and nonlinear signals generated with the FitzHugh-Nagumo (FHN) model, we system...