The prediction of the financial time series based on correlation dimension

Abstract

In this paper we firstly analysis the chaotic characters of three sets of the financial time series (Hang Sheng Index (HIS), Shanghai Stock Index and US gold price) based on the phase space reconstruction. But when we adopt the feedforward neural networks to predict those time series, we found this method run short of a criterion in selecting the training set, so we present a new method: using correlation dimension (CD) as the criterion . By the experiments, the method is proved effective. © Springer-Verlag Berlin Heidelberg 2005.