Time Series Forecasting with Multiple Deep Learners: Selection from a Bayesian Network

作     者：Shusuke Kobayashi Susumu Shirayama 

作者机构：Graduate School of Engineering the University of Tokyo Tokyo Japan 

出 版 物：《Journal of Data Analysis and Information Processing》 (数据分析和信息处理（英文）)

年 卷 期：2017年第5卷第3期

页      面：115-130页

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

摘      要：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.