Classification of Stochastic Processes with Topological Data Analysis
This work addresses the challenge of improving classification accuracy for time series data, which is incremental as it applies an existing method (Topological Data Analysis) to a specific domain.
The study tackled the problem of classifying time series from different stochastic processes using engineered topological features, finding that machine learning models based on these features consistently outperformed those using standard statistical and raw features in classification tasks.
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.