计算机工程 ›› 2013, Vol. 39 ›› Issue (2): 77-80.doi: 10.3969/j.issn.1000-3428.2013.02.015

• 网络与通信 • 上一篇    下一篇

基于小波和ARIMA模型的业务流预测方法

谢立春   

  1. (浙江工业职业技术学院电气工程学院,浙江 绍兴 312000)
  • 收稿日期:2012-03-26 修回日期:2012-05-24 出版日期:2013-02-15 发布日期:2013-02-13
  • 作者简介:谢立春(1974-),男,副教授,主研方向:网络优化,自动控制
  • 基金项目:
    浙江省自然科学基金资助项目(y1080023)

Traffic Flow Prediction Method Based on Wavelet and ARIMA Model

XIE Li-chun   

  1. (College of Electrical Engineering, Zhejiang Industry Polytechnic College, Shaoxing 312000, China)
  • Received:2012-03-26 Revised:2012-05-24 Online:2013-02-15 Published:2013-02-13

摘要: 针对因节点失效而造成的业务流性能变化问题,提出一种新的Ad hoc网络状态预测算法TAP。该算法利用小波变换减弱实际业务流的长相关特性,并结合自回归移动平均(ARIMA)模型和Kalman滤波建立状态预测方程。通过仿真实验对比分析ARMA和FARIMA的预测精度,结果表明,TAP算法业务流性能较优,其残差为18.23%。

关键词: 长相关, 预测, 小波, 自回归移动平均模型, Kalman滤波, 精度

Abstract: Aiming at the problem of the traffic flow performance by node failure, a novel status prediction algorithm Trous-based ARIMA Prediction(TAP) of Ad hoc network is proposed. In this algorithm, wavelet transform is adopted to ease up the long dependence of actual traffic, and the status prediction formula is built by Autoregressive Integrated Moving Average(ARIMA) model and Kalman filter. A simulation is conducted to study the accuracy between TAP and ARIMA, as well as FARIMA. Results show that TAP has better performance, and the residual is 18.23%.

Key words: long dependence, prediction, wavelet, Autoregressive Integrated Moving Average(ARIMA) model, Kalman filtering, accuracy

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