Uncertain time series analysis with imprecise observations

Xiangfeng Yang () and Baoding Liu ()
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Xiangfeng Yang: University of International Business and Economics
Baoding Liu: Tsinghua University

Fuzzy Optimization and Decision Making, 2019, vol. 18, issue 3, No 1, 263-278

Abstract: Abstract 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.

Keywords: Time series analysis; Uncertainty theory; Principle of least square; Residual analysis; Confidence interval (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s10700-018-9298-z

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