Forecasting the Nikkei Stock Average using Deep Belief Network

Abstract

In this study, we propose a new forecasting method of a financial time series based on Deep Belief Network (DBN) by enhancing the approach of the Chao et al. First, a new topology for a regression training is proposed. Second, we forecast a Nikkei Stock Average renewing a training term. Third, Self-Organized-Map (SOM) is introduced for reducing the computational time in DBN. It is shown by some experiments that some improved performance indexes can be obtained, and reduction of the computation time is achieved.