基于LFSN和小波变换的业务流预测算法

摘要

Aiming at the problem of low prediction accuracy of actual service flow,a novel State Prediction algorithm based on Wavelet transform and LFSN ( SPWL ) is proposed by Linear Fractional Stable Noise ( LFSN ) and wavelet transform. In this algorithm,the characteristic of LFSN distribution is defined,and the fitting method of actual data which is satisfied LFSN progress is given with discrete Fourier transform. The prediction accuracy is improved by fusing the results of LFSN model. Simulation is conducted to study the key influence factor of algorithm with NS2 and Matlab,such as time delay, dropping rate, as well as utilization rate. Results show that, compared to FARIMA algorithm, SPWL algorithm has high accuracy prediction,the prediction error of SPWL is 12. 83%.%针对实际业务流预测精度偏低的问题,结合线性分形稳定运动( LFSM)模型和小波变换提出一种新的业务流预测算法( SPWL)。定义线性分形稳定噪声( LFSN)分布特征,利用离散傅里叶变换产生满足LFSN过程的数列,并给出实际业务流数据拟合方法。通过小波变换降低实际业务流的突发特性,同时融合LFSM模型的预测结果提高实际业务流的预测精度。基于NS2和Matlab进行仿真实验,结果表明,与FARIMA算法相比,SPWL算法预测精度较高,其预测误差仅为12．83%。