一种自适应网络舆情演化建模方法

Adaptive Evolution Modeling Method of Internet Public Opinions

针对短期趋势预测方法忽略演化过程统计特性的动态变化性,致使模型选择盲目、预测效果较差的问题,本文提出一种自适应网络舆情演化建模方法(AEMIPO)。首先,动态跟踪网络舆情演化过程的平稳性、周期性和自相似性等统计特性;其次,选取能够描述上述统计特性的ARMA,ARIMA,SARIMA,FARIMA模型构建备选模型库;最后,通过制定模型选择规则,从备选模型库中选择合适的模型对当前时刻的演化过程进行自适应建模,并预测其演化趋势。实验表明,与现有方法相比,AEMIPO具有更高的预测精度与更好的预测稳定性,更适合对网络舆情演化过程进行短期建模及趋势预测。

The existing short-term trend forecasting method ignores the variability of statistical properties of Internet public opinionsr evolution, which leads to a blind model selection, and the poor forecasting performance. Therefore, an adaptive evolution modeling method of Internet public opinions （AEMIPO） is presented. Firstly, the method tracks the statistical characteristics of the process of Internet public opinionsr evolution dynamically, such as smoothness, periodicity and self-similarity. Then, by selecting ARMA, ARIMA, SARIMA and FARIMA, an alternative model bank is constructed. Finally, by making model selection rules, an appropriate model is selected to model and forecast the evolution process adaptively. Experimental results show that compared with the existing methods, AEMIPO has higher forecasting accuracy and stability, and the method is more suitable for short-term modeling and forecasting of Internet public opinions＇ evolution trend.