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.
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Pravilovic, S., Appice, A., Malerba, D. (2014). Integrating Cluster Analysis to the ARIMA Model for Forecasting Geosensor Data. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_24
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DOI: https://doi.org/10.1007/978-3-319-08326-1_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08325-4
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