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An approximated principal component prediction model for continuous‐time stochastic processes

Author

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  • Ana M. Aguilera
  • Francisco A. Ocaña
  • Mariano J. Valderrama

Abstract

In this paper, a linear model for forecasting a continuous‐time stochastic process in a future interval in terms of its evolution in a past interval is developed. This model is based on linear regression of the principal components in the future against the principal components in the past. In order to approximate the principal factors from discrete observations of a set of regular sample paths, cubic spline interpolation is used. An application for forecasting tourism evolution in Granada is also included. © 1997 by John Wiley & Sons, Ltd.

Suggested Citation

  • Ana M. Aguilera & Francisco A. Ocaña & Mariano J. Valderrama, 1997. "An approximated principal component prediction model for continuous‐time stochastic processes," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 13(2), pages 61-72, June.
    • Handle: RePEc:wly:apsmda:v:13:y:1997:i:2:p:61-72
    DOI: 10.1002/(SICI)1099-0747(199706)13:23.0.CO;2-I
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    Cited by:

    1. Christian Acal & Manuel Escabias & Ana M. Aguilera & Mariano J. Valderrama, 2021. "COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression," Mathematics, MDPI, vol. 9(11), pages 1-23, May.
    2. Paula R. Bouzas & Nuria Ruiz-Fuentes & Carmen Montes-Gijón & Juan Eloy Ruiz-Castro, 2021. "Forecasting counting and time statistics of compound Cox processes: a focus on intensity phase type process, deletions and simultaneous events," Statistical Papers, Springer, vol. 62(1), pages 235-265, February.

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