Abstract
Detection of abrupt changes in time series data structure is very useful in modeling and prediction in many application areas, where time series pattern recognition must be implemented. Despite of the wide amount of research in this area, the proposed methods require usually a long execution time and do not provide the possibility to estimate the real changes in variance and autocorrelation at certain points. Hence they cannot be efficiently applied to the large time series where only the change points with constraints must be detected. In the framework of the present paper we provide heuristic methods based on the moving variance ratio and moving median difference for identification of change points. The methods were applied for behavior analysis of farm animals using the data sets of accelerations obtained by means of the radio frequency identification (RFID).
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Acknowledgements
This work has been supported by the Linz Center of Mechatronics (LCM) in the framework of the Austrian COMET-K2 programme and by Smartbow GmbH, which provided the real data sets.
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Breitenberger, S., Efrosinin, D., Auer, W., Deininger, A., Waßmuth, R. (2017). Change Point Detection in Piecewise Stationary Time Series for Farm Animal Behavior Analysis. In: Dörner, K., Ljubic, I., Pflug, G., Tragler, G. (eds) Operations Research Proceedings 2015. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-42902-1_50
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DOI: https://doi.org/10.1007/978-3-319-42902-1_50
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