Application of Spatio-Temporal Clustering in Forecasting Optimization of Geo-Referenced Time Series

Sonja Pravilovic^{1, 2,} and Annalisa Appice¹

¹Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro via Orabona, Bari, Italy

²Faculty of Information Technology, Mediterranean University, Vaka Djurovica b.b. Podgorica, Montenegro

Cite this paper:
Sonja Pravilovic and Annalisa Appice. Application of Spatio-Temporal Clustering in Forecasting Optimization of Geo-Referenced Time Series. American Journal of Modeling and Optimization. 2014; 2(1):8-15. doi: 10.12691/ajmo-2-1-2

Abstract

A novel field of data mining has been spatio-temporal clustering focused on the new methods and techniques, which are able to adapt previous methods and solutions to the new problems. A set of geo-referenced time series are data generated by several devices like GPS, sensor station, cell phones and many other sensing device. This paper defines the the new K-means clustering grouping spatially and temporally correlated geo-referenced time series obtained from sensors in a specific geographic area. For all time series in the cluster, choosing the best forecasting parameters, we apply one of the most accurate and most efficient forecasting models of time series called ARIMA. This paper investigates a new mechanism to determine spatio-temporal distances measure between sensor stations in the same spatio-temporal neighborhood (cluster). By calculating, the best forecasting parameters applied for all time series in the same cluster proposed algorithm obtains more accurate and more efficient forecasting results, than forecasting time series independently one from other in space and time. We studied the accuracy of proposed model comparing it to the already known applied to compute prediction of time series and applying it to real life data.

Keywords:
spatio-temporal clustering time-series ARIMA model

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