Time Series Prediction Using DBN and ARIMA

Abstract

Time series data analyze and prediction is very important to the study of nonlinear phenomenon. Studies of time series prediction have a long history since last century, linear models such as autoregressive integrated moving average (ARIMA) model, and nonlinear models such as multi-layer perceptron (MLP) are well-known. As the state-of-art method, a deep belief net (DBN) using multiple Restricted Boltzmann machines (RBMs) was proposed recently. In this study, we propose a novel prediction method which composes not only a kind of DBN with RBM and MLP but also ARIMA. Prediction experiments for the time series of the actual data and chaotic time series were performed, and results showed the effectiveness of the proposed method.