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Spatial prediction and spatial dependence monitoring on georeferenced data streams

Author

Listed:
  • Antonio Balzanella

    (Università della Campania Luigi Vanvitelli)

  • Antonio Irpino

    (Università della Campania Luigi Vanvitelli)

Abstract

This paper deals with the analysis of data streams recorded by georeferenced sensors. We focus on the problem of measuring the spatial dependence among the observations recorded over time and with the prediction of the data distribution, where no sensor record is available. The proposed strategy consists of two main steps: an online step summarizes the incoming data records by histograms; an offline step performs the measurement of the spatial dependence and the spatial prediction. The main novelties are the introduction of the variogram and the kriging for histogram data. Through these new tools we can monitor the spatial dependence and to perform the prediction starting from histogram data, rather than from sensor records. The effectiveness of the proposal is evaluated on real and simulated data.

Suggested Citation

  • Antonio Balzanella & Antonio Irpino, 2020. "Spatial prediction and spatial dependence monitoring on georeferenced data streams," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 101-128, March.
    • Handle: RePEc:spr:stmapp:v:29:y:2020:i:1:d:10.1007_s10260-019-00462-0
    DOI: 10.1007/s10260-019-00462-0
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    References listed on IDEAS

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    1. Menafoglio, Alessandra & Secchi, Piercesare, 2017. "Statistical analysis of complex and spatially dependent data: A review of Object Oriented Spatial Statistics," European Journal of Operational Research, Elsevier, vol. 258(2), pages 401-410.
    2. Billard L. & Diday E., 2003. "From the Statistics of Data to the Statistics of Knowledge: Symbolic Data Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 470-487, January.
    3. González-Rivera, Gloria & Arroyo, Javier, 2012. "Time series modeling of histogram-valued data: The daily histogram time series of S&P500 intradaily returns," International Journal of Forecasting, Elsevier, vol. 28(1), pages 20-33.
    4. Menafoglio, Alessandra & Petris, Giovanni, 2016. "Kriging for Hilbert-space valued random fields: The operatorial point of view," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 84-94.
    5. Pigoli, Davide & Menafoglio, Alessandra & Secchi, Piercesare, 2016. "Kriging prediction for manifold-valued random fields," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 117-131.
    6. Arroyo, Javier & Maté, Carlos, 2009. "Forecasting histogram time series with k-nearest neighbours methods," International Journal of Forecasting, Elsevier, vol. 25(1), pages 192-207.
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