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- W2746969437 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." @default.
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- W2746969437 date "2017-01-01" @default.
- W2746969437 modified "2023-09-24" @default.
- W2746969437 title "Time Series Forecasting with Multiple Deep Learners: Selection from a Bayesian Network" @default.
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- W2746969437 doi "https://doi.org/10.4236/jdaip.2017.53009" @default.
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