Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Andrienko, G., Andrienko, N.: Interactive cluster analysis of diverse types of spatiotemporal data. SIGKDD Explor. Newsl. 11(2), 19–28 (2010)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Birant, D., Kut, A.: St-dbscan: An algorithm for clustering spatial temporal data. Data and Knowledge Engineering 60(1), 208–221 (2007)CrossRefGoogle Scholar
  3. 3.
    Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control, 3rd edn. Prentice-Hall (1994)Google Scholar
  4. 4.
    Brockwell, P., Davis, R.: Time Series: Theory and Methods, 2nd edn. Springer (2009)Google Scholar
  5. 5.
    Hyndman, R., Khandakar, Y.: Automatic time series forecasting: The forecast package for r. Journal of Statistical Software 26(3) (2008)Google Scholar
  6. 6.
    Kamarianakis, Y., Prastacos, P.: Space-time modeling of traffic flow. Comput. Geosci. 31(2), 119–133 (2005)CrossRefGoogle Scholar
  7. 7.
    Kwiatkowski, D., Phillips, P.C., Schmidt, P., Shin, Y.: Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics (54), 159–178 (1992)Google Scholar
  8. 8.
    Orkin, R.D.M.: Vital Statistics. McGraw-Hill, New York (1990)Google Scholar
  9. 9.
    Pravilovic, S., Appice, A., Malerba, D.: An intelligent technique for forecasting spatially correlated time series. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds.) AI*IA 2013. LNCS, vol. 8249, pp. 457–468. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Qin, K., Chen, Y., Zhan, Y., Cheng, F.: Spatial clustering considering spatio-temporal correlation. In: 2011 19th International Conference on Geoinformatics, pp. 1–4 (2011)Google Scholar
  11. 11.
    Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., Andrienko, G.: Visually driven analysis of movement data by progressive clustering. Information Visualization 7(3), 225–239 (2008)CrossRefGoogle Scholar
  12. 12.
    Rousseeuw, P.J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics 20, 53–65 (1987)CrossRefzbMATHGoogle Scholar
  13. 13.
    Sershenfeld, N.A., Weigend, A.S.G.: The future of time series. In: Gershenfeld, A.N., Weigen, A.S. (eds.) Time Series Prediction: Forecasting the Future and Understanding the Past, pp. 1–70 (1993)Google Scholar
  14. 14.
    Tobler, W.: A computer movie simulating urban growth in the Detroit region. Economic Geography 46(2), 234–240 (1970)CrossRefGoogle Scholar
  15. 15.
    Trasarti, R.: Mastering the Spatio-Temporal Knowledge Discovery Process. PhD thesis. University of Pisa Department of Computer Science, Italy (2010)Google Scholar
  16. 16.
    Zhang, P., Huang, Y., Shekhar, S., Kumar, V.: Correlation analysis of spatial time series datasets: A filter-and-refine approach. In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS (LNAI), vol. 2637, pp. 532–544. Springer, Heidelberg (2003)Google Scholar

Copyright information

© 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

Personalised recommendations