Time Series Forecasting by Principal Component Methods

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.