PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series
This work addresses the challenge of changepoint detection in time series with complex dependencies for researchers and practitioners in fields like statistics and data science, offering a tool that relaxes common assumptions, though it is incremental as it builds on existing nonparametric methods.
The authors tackled the problem of detecting distributional changes in piece-wise stationary time series with long-range dependencies, introducing PyChEst, a Python package that provides consistent nonparametric algorithms for multiple changepoint estimation without assumptions beyond stationarity, and demonstrated its performance by comparing it against state-of-the-art models designed for i.i.d. settings.
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.