Uncertain time series analysis with imprecise observations

被引：0
作者：
Xiangfeng Yang
Baoding Liu
机构：
[1] University of International Business and Economics,School of Information Technology and Management
[2] Tsinghua University,Department of Mathematical Sciences
来源：
Fuzzy Optimization and Decision Making | 2019年 / 18卷
关键词：
Time series analysis; Uncertainty theory; Principle of least square; Residual analysis; Confidence interval;
D O I：
暂无
中图分类号：
学科分类号：
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.
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页码：263 / 278
页数：15
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