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In this paper a new method for detecting multiple structural breaks, i.e. undesired changes of signal behavior, is presented and applied to real-world data. It will be shown how Chernoff Bounds can be used for highperformance hypothesis testing after preprocessing arbitrary time series to binary random variables using k-means-clustering. Theoretical results from part one of this paper have been applied to real-world time series from a pharmaceutical wholesaler and show striking improvement in terms of forecast error reduction, thereby greatly improving forecast quality. In order to test the effect of structural break detection on forecast quality, state of the art forecast algorithms have been applied to time series with and without previous application of structural break detection methods.

FORECAST ERROR REDUCTION BY PREPROCESSED HIGH-PERFORMANCE STRUCTURAL BREAK DETECTION