计算机工程与应用Issue(16)：240-243,4.DOI:10.3778/j.issn.1002-8331.1112-0011
冥函数变换在短时交通流组合预测中的应用
Application of power function on short-term traffic flow prediction
摘要
Abstract
Traffic flow is a non-linear time series with obvious noise. For this feature, a conversion method using power func-tion is proposed to the time series, after the transformation, the noise than the original signal will be a greater degree of compres-sion. And reduce the adverse effects of the predicted results. Use LS-SVM to compensation loop for the prediction of ARIMA. Use the inverse transform method to restore the signal output. The results show that the combination model based on the conver-sion of power function possesses satisfactory accuracy.关键词
冥函数变换/自回归求和滑动平均模型(ARIMA)/最小二乘支持向量机(LS-SVM)/短时交通流预测Key words
power function/Autoregressive Integrated Moving Average Model(ARIMA)/Least Squares Support Vector Ma-chines(LS-SVM)/short-term traffic flow prediction分类
信息技术与安全科学引用本文复制引用
李宁,王晓东,侯俊峰,黄国勇..冥函数变换在短时交通流组合预测中的应用[J].计算机工程与应用,2013,(16):240-243,4.基金项目
云南省应用基础研究计划基金（No.2011FZ036）；云南省教育厅基金（No.2011Y386）。 （）
