✦ LIBER ✦

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

✍ Scribed by Aguilera, Ana M. ;Ocaña, Francisco A. ;Valderrama, Mariano J.

Publisher: John Wiley and Sons
Year: 1997
Tongue: English
Weight: 196 KB
Volume: 13
Category: Article
ISSN: 8755-0024
DOI: 10.1002/(sici)1099-0747(199706)13:2<61::aid-asm296>3.0.co;2-i

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✦ Synopsis

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.