电商大数据网络指数统计建模与分析.pdfVIP

Statistics and Application 统计学与应用, 2016, 5(2), 196-202 The Statistical Modeling and Analysis of Electricity Business Big Data Network Index Fei Wang, Hui Mei, Ruili Zhang, Chengping Gong, Xiong Xie, Liyun Su* College of Mathematics and Statistics, Chongqing University of Technology, Chongqing th th th Received: Jun. 9 , 2016; accepted: Jun. 24 , 2016; published: Jun. 30 , 2016 Copyright © 2016 by authors and Hans Publishers Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). Abstract This article first briefly describes the present situation of the big e-commerce data age, and chooses Baidu and Ali index to explain its concept. Based on the Ali index of smartphone industry, we analyze the data from January 1, 2016 to March 3, 2016. Using SAS software, the stationarity and randomness of the sequence are researched. Then we carry on model fitting and forecast of two stationary white noise sequences to conclude the model expression and calculate the forecast error. The result shows that the conclusion can provide a certain reference value for the purchas- ers and suppliers of 1688. Keywords Electricity Business Big Data, Ali Index, Time Series Analysis, Baidu Index 电商大数据网络指数统计建模与分析 王 飞，梅 辉，张瑞丽，龚铖萍，谢 熊，苏理云* 重庆理工大学数学与统计学院，重庆 收稿日期：2016年6月9 日；录用日期：2016年6月24 日；发布日期：2016年6月30 日 摘 要 本文首先简述电商大数据时代的现状，选取百度指数和阿里指数，对其概念进行说明。基于智能手机行 文章引用: 王飞, 梅辉, 张瑞丽, 龚铖萍, 谢熊, 苏理云. 电商大数据网络指数统计建模与分析[J]. 统计学与应用, 2016, 5(2): 196-202. /10.12677/sa.2016.52019 王飞 等 业的阿里指数分析了自2016年1月1 日至2016年3月3 日的数据，运用SAS软件编程，研究了序列的平稳 性和随机性，并对得到的两个平稳非白噪声序列进行模型拟合和预测，最后得出模型表达式并计算了预 测误差。结果发现，所得出的结论能为1688采购商