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arXiv · 2008.07709

Selecting Data Adaptive Learner from Multiple Deep Learners using Bayesian Networks

Shusuke Kobayashi·Susumu Shirayama
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Abstract

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.

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BibTeXRIS

Shusuke Kobayashi, Susumu Shirayama. 2020-08-18. Selecting Data Adaptive Learner from Multiple Deep Learners using Bayesian Networks. https://doi.org/10.1007/s00521-020-05234-6

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