Derivation of a state-space model by functional data analysis
A state-space model is proposed for prediction of continuous stochastic processes observed at discrete points. It is based on spline-interpolation of the original process approximated by Karhunen-Loève expansions and Kalman-Bucy filtering techniques. Results of simulations for narrow-band processes and applications to Madrid stock market data are presented.
scientific article; zbMATH DE number 2118519