Extended Set Covering for Time Series Segmentation

Time series analysis plays a critical role in data analytics, an effective modeling of nonlinear trends is essential for obtaining actionable results, notably for forecasting and missing values imputation. The segmentation of time series and the corresponding detection of change points stand out for their practical implications. This paper presents preliminary results of a study on the applicability of mathematical programming, and in particular matheuristics, to time series segmentation.