基于灰色马尔可夫Verhulst模型的因特网访问人数预测分析

Prediction and analysis of the Internet access population based on gray Markov Verhulst model

为了科学准确地预测近几年因特网访问人数，提出了应用灰色马尔可夫Verhulst模型进行预测的方法。首先，利用历史数据建立灰色Verhulst模型，通过确定系数可获得因特网访问人数的时间响应序列的表达式，从而可获得未来年份因特网访问人数的发展序列值。然后，结合马尔可夫链过程将序列状态划分为三类，通过确定状态转移矩阵可获得序列处于各状态的概率值及与各状态对应的预测中值，最终求得各序列的修正值。最后，通过2006／12～2012／6期间我国互联网上网人数的历史数据，预测了最近四个统计时段的访问人数。实例表明，该模型预测结果的误差更小、精度更高，还能提供预测结果的波动范围及出现概率，能够为网络建设及管理提供决策依据。

In order to predict the Internet access population accurately, a forecasting method based on Gray Markov Verhulst model is proposed. The method uses historical data to construct gray Verhulst model, gets the expression of time response series of the Internet access population by determining coefficients, and obtains the development sequences of the Internet access population in the near future. Based on the Markov chain, the sequence states are divided into three parts, the state probability and medium prediction value are obtained by determining the state transition matrix, and further the modifi- cation values of each sequence are obtained. Finally, the Internet access population from December 2006 to June 2012 is used as original data to establish the forecasting model so as to predict the number of In- ternet users from December 2012 to June 2014. The results show that the prediction accuracy of the gray Markov Verhulst forecasting model has fewer errors and better prediction precision, gives the fluctuation range and the probability of the prediction results, and provides the decision-making basis for network construction and management.