Classification of stochastic processes with topological data analysis

Abstract

SummaryIn this study, we demonstrate that 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 on features coming from descriptive statistics and the wavelet transform. We conclude that machine learning models built on engineered topological features alone perform consistently better than those built on the standard statistical and wavelet features for time series classification tasks. We also apply dimension reduction techniques to our engineered features and compare the result of our classification models before and after dimensionality reduction. Finally, we also show that in our calculations of the engineered topological features, employing parallel computing methods does yield significant improvements in run time and memory footprint.

Special issue on High‐Performance Computing Conference (BASARIM 2022)

“…Güzel and Kaygun 6 propose an effective, novel approach to distinguishing time series samples from different stochastic processes. Thanks to the speedups obtained via parallel computing, the authors harness the power of engineered topological features, which outperform traditional statistical and wavelet features in building machine learning classification models.…”