Bayesian Change Point Dectection under Complex Time Series in Python Machine Learning Client for SAP HANA

A complex time series in real life usually has many change points inside it. When dealing with such data, simply applying traditional seasonality test to it may not render a convincing decomposition result. In this blog post, we will show how to use Bayesian Change Point Detection in the Python machine learning client for SAP HANA(hana-ml) to detect those change points and decompose the target time series. Time series may not ideally contain monotonic trend and seasonal waves after decomposition. On the contrary, it may include a great many inner change points in those parts.