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

Ana M. Aguilera, Francisco A. Ocaña and Mariano J. Valderrama

Applied Stochastic Models and Data Analysis, 1997, vol. 13, issue 2, 61-72

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.

Date: 1997
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https://doi.org/10.1002/(SICI)1099-0747(199706)13:23.0.CO;2-I

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmda:v:13:y:1997:i:2:p:61-72

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