  A method to predict time-series using multiple deep learners and a Bayesian
network is proposed. In this study, the input explanatory variables are
Bayesian network nodes that are associated with learners. Training data are
divided using K-means clustering, and multiple deep learners are trained
depending on the cluster. A Bayesian network is used to determine which deep
learner is in charge of predicting a time-series. We determine a threshold
value and select learners with a posterior probability equal to or greater than
the threshold value, which could facilitate more robust prediction. The
proposed method is applied to financial time-series data, and the predicted
results for the Nikkei 225 index are demonstrated.
