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