PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series

TL;DR

PyChEst is a Python package that accurately detects multiple distributional change points in complex, long-range dependent, piece-wise stationary time series without strict assumptions, outperforming existing models in various scenarios.

Contribution

The paper introduces PyChEst, a Python package implementing nonparametric, provably consistent algorithms for detecting multiple change points in general piece-wise stationary processes.

Findings

Algorithms are consistent even with long-range dependencies.

PyChEst outperforms state-of-the-art models in various tests.

No assumptions beyond stationarity are needed for change detection.

Abstract

We introduce PyChEst, a Python package which provides tools for the simultaneous estimation of multiple changepoints in the distribution of piece-wise stationary time series. The nonparametric algorithms implemented are provably consistent in a general framework: when the samples are generated by unknown piece-wise stationary processes. In this setting, samples may have long-range dependencies of arbitrary form and the finite-dimensional marginals of any (unknown) fixed size before and after the changepoints may be the same. The strength of the algorithms included in the package is in their ability to consistently detect the changes without imposing any assumptions beyond stationarity on the underlying process distributions. We illustrate this distinguishing feature by comparing the performance of the package against state-of-the-art models designed for a setting where the samples are independently and identically distributed.