Classification of stochastic processes with topological data analysis

In this study, we examine if engineered topological features can distinguish time series sampled from different stochastic processes with noise characteristics, in both balanced and unbalanced sampling schemes. We compare our classification results against the of same tasks built on statistical raw features. conclude that series, machine learning models perform consistently better than those standard