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Ribeiro, B. and Lopes, N. (2011) Deep Belief Networks for Financial Prediction. Lecture Notes in Computer Science, 7064, 766-773.
https://doi.org/10.1109/IJCNN.2011.6033368

has been cited by the following article:

  • 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.