话务量是一种受多重因素影响的动态非线性时间序列,采用传统的预测方法难以实现高精度的预测,为了提高预测精度,文章根据采集所得每日忙时话务量数据,分析其混沌特性和可预测性,并重构话务量时间系列的相空间,提出采用加权一阶局域预测方法来建立其预测模型,并进一步预测,仿真结果表明,该方法能够较好的拟合话务量的变化规律,具有较高的精度,为话务量的预测提供了一种有效的途径. Traffic is a dynamic nonlinear time series influenced by multiple factors. It is difficult to achieve high-precision prediction by traditional prediction methods. In order to improve the prediction accuracy, the article analyzes the chaotic Characteristics and predictability of the traffic volume, and reconstructs the phase space of the traffic time series. We propose a weighted first-order local prediction method to establish its prediction model and further predict. The simulation results show that this method can be better fitted The change rule of traffic volume has higher accuracy and provides an effective way for traffic prediction.