Author(s)

Shusuke Kobayashi, Susumu Shirayama

Graduate School of Engineering, the University of Tokyo, Tokyo, Japan.

Considering the recent developments in deep learning, it has become increasingly important to verify what methods are valid for the prediction of multivariate time-series data. In this study, we propose a novel method of time-series prediction employing multiple deep learners combined with a Bayesian network where training data is divided into clusters using K-means clustering. We decided how many clusters are the best for K-means with the Bayesian information criteria. Depending on each cluster, the multiple deep learners are trained. We used three types of deep learners: deep neural network (DNN), recurrent neural network (RNN), and long short-term memory (LSTM). A naive Bayes classifier is used to determine which deep learner is in charge of predicting a particular time-series. Our proposed method will be applied to a set of financial time-series data, the Nikkei Average Stock price, to assess the accuracy of the predictions made. Compared with the conventional method of employing a single deep learner to acquire all the data, it is demonstrated by our proposed method that F-value and accuracy are improved.

Time-Series Data, Deep Learning, Bayesian Network, Recurrent Neural Network, Long Short-Term Memory, Ensemble Learning, K-Means

Kobayashi, S. and Shirayama, S. (2017) Time Series Forecasting with Multiple Deep Learners: Selection from a Bayesian Network. Journal of Data Analysis and Information Processing, 5, 115-130. doi: 10.4236/jdaip.2017.53009.