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

Jindong Chen1,2, Yuxuan Du1,2, Linlin Liu1,2

  • 1School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China.

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|December 3, 2020
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Summary
This summary is machine-generated.

This study introduces SampEn-DNN, a novel method combining sample entropy and deep neural networks for forecasting Bulletin Board System (BBS) posts. This approach enhances prediction accuracy for public opinion monitoring and resource allocation.

Keywords:
BBS postsdeep neural networkssample entropytime series

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Area of Science:

  • Data Science
  • Computational Social Science
  • Time Series Analysis

Background:

  • Monitoring public opinion via Bulletin Board System (BBS) posts is vital for government agencies, corporations, and website operators.
  • Accurate forecasting of BBS post volume aids in timely decision-making and resource allocation for potential events.

Purpose of the Study:

  • To propose a novel approach, SampEn-DNN, for enhanced modeling and forecasting of BBS post time series.
  • To improve the accuracy and efficiency of predicting daily new posts on online platforms.

Main Methods:

  • The SampEn-DNN approach integrates sample entropy (SampEn) to determine optimal input vectors for deep neural networks (DNN).
  • DNN is utilized to enhance the predictive performance of the time series models.
  • Tianya Zatan new posts dataset was used for empirical evaluation.

Main Results:

  • The SampEn-DNN method demonstrated superior performance compared to traditional methods like ARIMA, seasonal ARIMA, polynomial regression, and standard neural networks.
  • Experimental results confirmed the effectiveness of SampEn-DNN in modeling and forecasting BBS post time series.

Conclusions:

  • The proposed SampEn-DNN approach offers a significant advancement in BBS post time series analysis.
  • This method provides a more accurate and reliable tool for predicting online public discourse and managing platform resources.