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

• 作者: Shusuke Kobayashi Susumu Shirayama
• 来源期刊: 数据分析和信息处理(英文)
• 学科: 工学
• 关键词: Time-Series Data DEEP LEARNING Bayesian NETWORK Recurrent Neural NETWORK Long Short-Term Memory ENSEMBLE LEARNING K-Means
• 年，卷（期）: 2017,（3）
• 页码范围: 115-130
• 页数: 16页
• 分类号: TP39

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.