聚类再回归方法在机场噪声时间序列预测中的应用  

Application of Cluster Regression in Time Series Prediction of Airport Noise

作　　者：夏利[1] 王建东[1] 张霞[1] 王丽娜[1] 

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京210016

出　　处：《数据采集与处理》2014年第1期152-156,共5页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金重点(61139002)资助项目

摘　　要：对于机场噪声的预测,针对绘制等值线方法预测成本高和误差较大的缺点,以及分类再回归方法中分类时缺乏可指导性标准的问题,本文提出了基于支持向量机的先聚类、再回归的时间序列的预测方法。对机场噪声时间序列的先聚类再回归方法,采用常用k均值划分算法,利用聚类特点,将样本限定在同一类的范围内,再对同类样本进行回归预测。Housing及Laser generated data数据集上的实验表明,采用先聚类再回归方法得到的拟合值比直接回归方法得到的拟合值要精确。将该方法应用到北京某机场实测数据中,并与其他预测模型进行对比,准确度明显优于其他预测方法。For airport noise prediction, aiming at the high cost and large error of contour draw- ing, as well as the lack of guidance standard in regression method based on SVM classification, it presents a method of cluster regression based on support vector machine （SVM）. Cluster re- gression in prediction of airport noise, using k-means algorithm, takes advantage of the charac- teristics of clustering. It firstly limits the sample within the same class, and then performs re- gression in the similar class. Experimental results on housing data set and Laser generated data set show that the fitted values of the cluster regression method are more accurate than the di- rect regression method. Applied the method to measured data of an airport in Beijing, and compared it with other prediction models, the accuracy of cluster regression is superior to that of other prediction methods.

关 键 词：支持向量机 时间序列 机场噪声 聚类 回归 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]