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A method to predict time-series using multiple deep learners and a Bayesiannetwork is proposed. In this study, the input explanatory variables areBayesian network nodes that are associated with learners. Training data aredivided using K-means clustering, and multiple deep learners are traineddepending on the cluster. A Bayesian network is used to determine which deeplearner is in charge of predicting a time-series. We determine a thresholdvalue and select learners with a posterior probability equal to or greater thanthe threshold value, which could facilitate more robust prediction. Theproposed method is applied to financial time-series data, and the predictedresults for the Nikkei 225 index are demonstrated.

Selecting Data Adaptive Learner from Multiple Deep Learners using Bayesian Networks