Πολυμεταβλητή ανάλυση χρονικών σειρών
Abstract
In this dissertation we examine the problem of classification analysis for time series data from its predictive aspect. We suggest two classification functions for time series through a linear stale-space model representation and, also, for non-linear time series of the GARCH family. Our classification functions are based on the likelihood ratio and the Kullback-Leibler information measure. We propose asymptotic distributions and we investigate their behaviour through simulation experiments.
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