统计与决策2018，Vol.34Issue(16)：13-17,5.DOI:10.13546/j.cnki.tjyjc.2018.16.003
水资源消耗预测的异常值检测及缺失数据填补方法
Method for Abnormal Data Detection and Missing Data Filling in Water Resources Consumption Forecasting
摘要
Abstract
Historical time-series data of water resources consumption should be complete and reliable so that it can be used for forecasting. On the basis of referring to the existing methods of abnormal data detection and missing data filling, this paper uses partial least square method to extract the principal component of water resource consumption and socio-economic related index data and draw the elliptical diagram of its cumulative contribution degree Q2 to identify its abnormal value. And then the paper employs the least residual regression method to predict the time series data with actual mutation, and constructs the least square support vector machine (SVM) model based on particle swarm optimization to fill the missing data. Results show that the total contribution of principal components calculated by partial least square and the method of drawing Q2 elliptical diagram can be utilized to predict abnormal points with the help of the stretching effect of outliers on the whole data; the minimum residual error regression method has higher precision than traditional least square method in the prediction of water resource consumption mutation data series; while use of least squares SVM for particle swarm optimization can further improve data fitting effect and realize a reasonable filling of missing data of water resource consumption.关键词
水资源消耗/异常值/缺失值/数据检测/数据填补Key words
water resources consumption/abnormal value/missing data/data detection/data filling分类
建筑与水利引用本文复制引用
张峰,宋晓娜,薛惠锋,王海宁..水资源消耗预测的异常值检测及缺失数据填补方法[J].统计与决策,2018,34(16):13-17,5.基金项目
国家自然科学基金资助项目(71371112) （71371112）
国家自然科学基金重点项目(U1501253) （U1501253）
广东省省级科技计划项目(2016B010127005) （2016B010127005）
山东省自然科学基金资助项目(ZR2012GM020) （ZR2012GM020）
中央分成水资源费项目(2016H22SK041) （2016H22SK041）
