TITLE:
Time Series Forecasting with Multiple Deep Learners: Selection from a Bayesian Network
AUTHORS:
Shusuke Kobayashi, Susumu Shirayama
KEYWORDS:
Time-Series Data, Deep Learning, Bayesian Network, Recurrent Neural Network, Long Short-Term Memory, Ensemble Learning, K-Means
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.5 No.3,
August
29,
2017
ABSTRACT: 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.