TIME SERIES MODELS FOR COMPOSITIONAL DATA

摘要：

　　Compositional time series are becoming widely applicable in many situations.Differing from traditional methods, that compositional data are firstly removed the constraints, and then standard time series techniques are used on the transformed data, we consider the compositional data series models built on the original compositional data.In this paper, C-white noise and C-stationary processes, based on inner products of compositional data in Simplex space, are defined.The C-AR model, C-MA model, C-ARMA model and C-ARIMA model processes are then deduced.

关键词： Compositional time series Simplex space ARIMA models

作者: Liying Shangguan Huiwen Wang Lynne Billard

作者单位: School of Economics and Management, Beihang University, Beijing 100191, China Department of Statistics, University of Georgia, Athens, GA 30602, USA

会议类型: 国际会议

会议名称: The 12th International Conference on Industrial Management(第十二届工业管理国际会议)

会议地点: 成都

会议语种:英文

页码: 297-301

在线出版日期: 2014-09-03（万方平台首次上网日期，不代表论文的发表时间）