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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This research was supported by Project PB96-1436, Dirección General de Enseñanza Superior, Ministerio de Educación y Cultura, Spain.
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Aguilera, A.M., Ocaña, F.A. & Valderrama, M.J. Forecasting with unequally spaced data by a functional principal component approach. Test 8, 233–253 (1999). https://doi.org/10.1007/BF02595871
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DOI: https://doi.org/10.1007/BF02595871