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Uncertain time series analysis with imprecise observations

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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.

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Acknowledgements

The authors gratefully acknowledge the financial support provided by National Natural Science Foundation of China (Grants Nos. 61573210 and 61873329).

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Authors and Affiliations

  1. School of Information Technology and Management, University of International Business and Economics, Beijing, 100029, China

    Xiangfeng Yang

  2. Department of Mathematical Sciences, Tsinghua University, Beijing, 100084, China

    Baoding Liu

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  1. Xiangfeng Yang
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  2. Baoding Liu
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Correspondence to Baoding Liu.

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Yang, X., Liu, B. Uncertain time series analysis with imprecise observations. Fuzzy Optim Decis Making 18, 263–278 (2019). https://doi.org/10.1007/s10700-018-9298-z

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