Time-series Data Prediction Using Multiple Deep Learners and Selection of the Learner by a Baysian Network

Abstract

With the development of the Deep Learning, it becomes more important to verify what methods are valid for the prediction of time series data. In this study, we propose a new method of time series prediction, using mulitiple deep learners and a Baysian network. In this paper, training data is divided into some clusters with K-means clustering and the multiple deep learners are trained, depending on each clusters. A naive Bayes classifier 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 Nikkei 225 is demonstrated.