Τεχνικές βελτιστοποίησης στην πρόβλεψη χρονοσειρών

Abstract

Time series prediction, especially in the case of financial time series, has attractedmajor research interest. In finance, time series analysis is applied widely for thepurposes of predicting prices of international and national markets; also it is used forthe prediction of financial crises. This thesis differences in the prediction’s objective;instead of focusing on the time series’ future price it aims on detecting the futuretime that the time series is expected to be locally optimized.This thesis introduces a backtracking techniques that integrates elements of specificoptimization techniques. The introduced variations of the technique generatemethodologies that confront the problem of the chronical allocation of a time series’local optima. Moreover, the technique provides a methodological frame for the examinationof the ex ante prediction of a financial crisis. Under the investment spectrumsuch information may provide a useful tool for the adoption of investment strategyand portfolio management.Finally the empirical research concludes with the application of the proposed techniquesto data deriving from major financial international markets and the domesticmarket, as well.