Forecasting with unequally spaced data by a functional principal component approach |
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| Authors: | Ana M. Aguilera Francisco A. Ocaña Mariano J. Valderrama |
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| Affiliation: | (1) Departamento de Estadística e Investigación Operativa Facultad de Farmacia, Universidad de Granada, Spain;(2) Dpto. de Estadística e I.O., Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain |
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| Abstract: | The Principal Component Regression model of multiple responses is extended to forccast a continuous-time stochastic process. Orthogonal projection on a subspace of trigonometric functions is applied in order to estimate the principal components using discrete-time observations from a sample of regular curves. The forecasts provided by this approach are compared with classical principal component regression on simulated data. |
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| Keywords: | Karhunen-Loève expansion least-squares linear prediction orthogonal projection principal components |
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