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On-line spatio-temporal prediction by a state space representation of the generalized space time autoregressive model

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  • Luigi Ippoliti

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  • Luigi Ippoliti, 2001. "On-line spatio-temporal prediction by a state space representation of the generalized space time autoregressive model," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1-2), pages 157-169.
    • Handle: RePEc:mtn:ancoec:2001:110
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    File URL: https://www.dss.uniroma1.it/RePec/mtn/articoli/2001-LIX-1_2-10.pdf
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    References listed on IDEAS

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    1. Kanti Mardia & Colin Goodall & Edwin Redfern & Francisco Alonso, 1998. "The Kriged Kalman filter," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 7(2), pages 217-282, December.
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    Cited by:

    1. Guillermo Ferreira & Jorge Mateu & Emilio Porcu, 2018. "Spatio-temporal analysis with short- and long-memory dependence: a state-space approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 221-245, March.
    2. Lara Fontanella & Luigi Ippoliti, 2003. "Dynamic models for space-time prediction via Karhunen-Loève expansion," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 61-78, February.
    3. Devi Munandar & Budi Nurani Ruchjana & Atje Setiawan Abdullah & Hilman Ferdinandus Pardede, 2023. "Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting," Mathematics, MDPI, vol. 11(13), pages 1-25, July.

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