This thesis investigates time series classification through the application of various algorithms on two distinct datasets, developed in collaboration with Eltek S.p.A. The research aims to assess the performance of these algorithms on both artificially generated and experimentally obtained data. The first case study involves an artificially generated dataset created via simulations to analyze the heat transport of a sensor immersed in a fluid under an applied voltage. The simulations were divided into four groups: one with normal Fourier heat transport and three with different models of anomalous transport, the primary objective was to recognize the four heat transport modelsusing temperature time series.