Integrating Cluster Analysis to the ARIMA Model for Forecasting Geosensor Data

  • Sonja Pravilovic
  • Annalisa Appice
  • Donato Malerba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)


Clustering geosensor data is a problem that has recently attracted a large amount of research. In this paper, we focus on clustering geophysical time series data measured by a geo-sensor network. Clusters are built by accounting for both spatial and temporal information of data. We use clusters to produce globally meaningful information from time series obtained by individual sensors. The cluster information is integrated to the ARIMA model, in order to yield accurate forecasting results. Experiments investigate the trade-off between accuracy and efficiency of the proposed algorithm.


Time Series Sensor Node ARIMA Model Time Series Forecast Density Base Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sonja Pravilovic
    • 1
    • 2
  • Annalisa Appice
    • 1
  • Donato Malerba
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di Bari Aldo MoroBariItaly
  2. 2.Faculty of Information TechnologyMediterranean UniversityPodgoricaMontenegro

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