混沌局域预测法在网络流量短期预测中的应用

网络流量数据表现出一定的混沌特性,而在传统的混沌预测算法中,使用欧氏距离衡量相空间中相点间的相关性.由于欧氏距离的局限性,在高维相空间中,传统混沌预测算法的精度迅速下降.使用夹角余弦取代欧氏距离,作为判别相点间相关性的标准;并将相点视为向量,以向量的模和夹角为优化目标.进行预测参数识别.将以上算法应用于网络流量数据的短期预测,结果表明,在高维相空间中,预测算法的精度得到了明显的提升.

Application of Chaotic Forecasting Algorism to Network Traffic Forecasting

The network traffic data is chaotic, and in traditional chaotic forecasting algorism, the Euclid distance is used to measure correlation between phase points in phase space. Because of limitation of Euclid distance, in high dimension phase space, the forecasting accuracy of the traditional chaotic forecasting algorism decreases rapidly. The included angle cosine instead of Euclid distance is used as the standard to judge correlation between phase points. And regarding phase points as vectors and taking vectors module and angles as optimization objectives, identifies forecasting parameters. Applies the above method to forecast the network traffic data and the results indicate that the forecasting accuracy increases remarkably in high dimension phase space.