caa a22 4500 475826493 CHVBK 20180406123830.0 cr unu---uuuuu 170329e20000301xx s 000 0 eng 10.1023/A:1004189000171 doi (NATIONALLICENCE)springer-10.1023/A:1004189000171 Grillenzoni Carlo IUAV: University Institute of Architecture of Venice, St. Croce 1957, 30135, Venezia, Italy aut Time-Varying Parameters Prediction [Elektronische Daten] [Carlo Grillenzoni] This paper develops a method of adaptive modeling that may be applied to forecast non-stationary time series. The starting point are time-varying coefficients models introduced in statistics, econometrics and engineering. The basic step of modeling is represented by the implementation of adaptive recursive estimators for tracking parameters. This is achieved by unifying basic algorithms—such as recursive least squares (RLS) and extended Kalman filter (EKF)—into a general scheme and next by selecting its coefficients with the minimization of the sum of squared prediction errors. This defines a non-linear estimation problem that may be analyzed in the context of the conditional least squares (CLS) theory. A numerical application on the IBM stock price series of Box-Jenkins illustrates the method and shows its good forecasting ability. The Institute of Statistical Mathematics, 2000 Conditional least squares nationallicence extended Kalman filter nationallicence IBM stock price series nationallicence recursive least squares nationallicence time-varying parameter models nationallicence Annals of the Institute of Statistical Mathematics Kluwer Academic Publishers 52/1(2000-03-01), 108-122 0020-3157 52:1<108 2000 52 10463 https://doi.org/10.1023/A:1004189000171 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1004189000171 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Grillenzoni Carlo IUAV: University Institute of Architecture of Venice, St. Croce 1957, 30135, Venezia, Italy aut NATIONALLICENCE 773 0- Annals of the Institute of Statistical Mathematics Kluwer Academic Publishers 52/1(2000-03-01), 108-122 0020-3157 52:1<108 2000 52 10463 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer