A Selection Method of Data Adaptive Learner from Multiple Deep Learners Using Bayesian Networks

Abstract

This paper proposes a new method of time series prediction, using mulitiple deep learners and a Baysian network. We firstly suggests two approaches. The former is a method in which explanatory variables of inputs data are nodes of a Bayesian network and are associated with learners. On the other hand, the latter method is a method in which the outputs of all the learners are made to nodes of the Bayesian network and the outputs are integrated. In this paper, the former method will be proposed in detail. Training data is divided into some clusters with K-means clustering and the multiple deep learners are trained, depending on each clusters. A Bayesian network is used to determine which the deep learner is in charge of predicting a time series. Our proposed method is applied to financial time series data, and the predicted results for the return of Nikkei 225 is demonstrated.