Abstract
On the basis of Functional Principal Component Analysis (FPCA), two forecasting approaches for time series are developed in this paper. The first one uses weighted multiple linear regression among principal components whereas the second one applies Kalman filtering on approximate state-space models. The forecasting performance of both methods is discussed on a real financial time-series.
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© 1998 Springer-Verlag Berlin Heidelberg
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Valderrama, M.J., Aguilera, A.M., Ruiz-Molina, J.C. (1998). Time Series Forecasting by Principal Component Methods. In: Payne, R., Green, P. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-01131-7_12
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DOI: https://doi.org/10.1007/978-3-662-01131-7_12
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1131-5
Online ISBN: 978-3-662-01131-7
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