Supporting Visual Parameter Analysis of Time Series Segmentation with Correlation Calculations

Abstract

Parameter analysis can be used to find out how individual parameters influence the output of an algorithm. We aim to support the visual parameter analysis of algorithms for the segmentation of time series. To this end, we automatically search for correlations between parameters and the segmentation outputs. Correlations are not only determined globally, but also locally within parameter subspaces. Calculated correlations are used to visually emphasize parameter and value ranges with high influence on the segmentation. By interactive exploration, the analyst can study the multidimensional parameter space in depth.