| Since 1970s, linear models such as ARIMA have been popular for time series data analyze and prediction. Meanwhile, artificial neural networks (ANNs), which are nonlinear models, inspired by connectionism bio-informatics, have been showing their powerful abilities of function approximation, pattern recognition, dimensionality reduction, and so on. Recently, deep belief nets (DBNs) which use multiple Restricted Boltzmann machines (RBMs) and multi-layered perceptron (MLP) are proposed as time series predictors. In this study, a hybrid prediction method with DBN and ARIMA is proposed. The effectiveness of the novel method was confirmed by the experiments using chaotic time series data, and CATS benchmark data. |