Uncertain time series analysis with imprecise observations

被引：100

作者：

Yang, Xiangfeng ^{[1
]}

Liu, Baoding ^{[2
]}

机构：

[1] Univ Int Business & Econ, Sch Informat Technol & Management, Beijing 100029, Peoples R China

[2] Tsinghua Univ, Dept Math Sci, Beijing 100084, Peoples R China

来源：

FUZZY OPTIMIZATION AND DECISION MAKING | 2019年 / 18卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Time series analysis; Uncertainty theory; Principle of least square; Residual analysis; Confidence interval; FORECASTING ENROLLMENTS; FUZZY; MODEL;

D O I：

10.1007/s10700-018-9298-z

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Time series analysis is a method to predict future values based on previously observed values. Assuming the observed values are imprecise and described by uncertain variables, this paper proposes an approach of uncertain time series. By employing the principle of least squares, a minimization problem is derived to calculate the unknown parameters in the uncertain time series model. In addition, residual and confidence interval are also proposed. Finally, some numerical examples are given.

引用

页码：263 / 278

页数：16

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