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

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 an...

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Detalles Bibliográficos
Autores principales: Chen, Jindong, Du, Yuxuan, Liu, Linlin, Zhang, Pinyi, Zhang, Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514164/
https://www.ncbi.nlm.nih.gov/pubmed/33266773
http://dx.doi.org/10.3390/e21010057
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author Chen, Jindong
Du, Yuxuan
Liu, Linlin
Zhang, Pinyi
Zhang, Wen
author_facet Chen, Jindong
Du, Yuxuan
Liu, Linlin
Zhang, Pinyi
Zhang, Wen
author_sort Chen, Jindong
collection PubMed
description 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.
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spelling pubmed-75141642020-11-09 BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks Chen, Jindong Du, Yuxuan Liu, Linlin Zhang, Pinyi Zhang, Wen Entropy (Basel) Article 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. MDPI 2019-01-12 /pmc/articles/PMC7514164/ /pubmed/33266773 http://dx.doi.org/10.3390/e21010057 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Jindong
Du, Yuxuan
Liu, Linlin
Zhang, Pinyi
Zhang, Wen
BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks
title BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks
title_full BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks
title_fullStr BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks
title_full_unstemmed BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks
title_short BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks
title_sort bbs posts time series analysis based on sample entropy and deep neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514164/
https://www.ncbi.nlm.nih.gov/pubmed/33266773
http://dx.doi.org/10.3390/e21010057
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