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Entropy 2019, 21(1), 57; https://doi.org/10.3390/e21010057

BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks

1,2
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1,2
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1,2
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and
3,4,*
1
School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China
2
Beijing Key Lab of Green Development Decision Based on Big Data, Beijing 100192, China
3
School of Economics and Management, Beijing University of Technology, Beijing 100124, China
4
School of Information Engineering, Xi’an University, Xi’an 710065, China
*
Author to whom correspondence should be addressed.
Received: 10 December 2018 / Revised: 4 January 2019 / Accepted: 8 January 2019 / Published: 12 January 2019
(This article belongs to the Special Issue The 20th Anniversary of Entropy - Approximate and Sample Entropy)
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Abstract

The modeling and forecasting of BBS (Bulletin Board System) posts time series is crucial for government agencies, corporations and website operators to monitor public opinion. Accurate prediction of the number of BBS posts will assist government agencies or corporations in making timely decisions and estimating the future number of BBS posts will help website operators to allocate resources to deal with the possible hot events pressure. By combining sample entropy (SampEn) and deep neural networks (DNN), an approach (SampEn-DNN) is proposed for BBS posts time series modeling and forecasting. The main idea of SampEn-DNN is to utilize SampEn to decide the input vectors of DNN with smallest complexity, and DNN to enhance the prediction performance of time series. Selecting Tianya Zatan new posts as the data source, the performances of SampEn-DNN were compared with auto-regressive integrated moving average (ARIMA), seasonal ARIMA, polynomial regression, neural networks, etc. approaches for prediction of the daily number of new posts. From the experimental results, it can be found that the proposed approach SampEn-DNN outperforms the state-of-the-art approaches for BBS posts time series modeling and forecasting. View Full-Text
Keywords: sample entropy; deep neural networks; BBS posts; time series sample entropy; deep neural networks; BBS posts; time series
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Chen, J.; Du, Y.; Liu, L.; Zhang, P.; Zhang, W. BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks. Entropy 2019, 21, 57.

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