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Uncertain regression model with autoregressive time series errors

  • Mathematical methods in data science
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Abstract

Uncertain regression model is a powerful analytical tool for exploring the relationship between explanatory variables and response variables. It is assumed that the errors of regression equations are independent. However, in many cases, the error terms are highly positively autocorrelated. Assuming that the errors have an autoregressive structure, this paper first proposes an uncertain regression model with autoregressive time series errors. Then, the principle of least squares is used to estimate the unknown parameters in the model. Besides, this new methodology is used to analyze and predict the cumulative number of confirmed COVID-19 cases in China. Finally, this paper gives a comparative analysis of uncertain regression model, difference plus uncertain autoregressive model, and uncertain regression model with autoregressive time series errors. From the comparison, it is concluded that the uncertain regression model with autoregressive time series errors can improve the accuracy of predictions compared with the uncertain regression model.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grants No.61873329 and 61873084).

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Authors and Affiliations

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

    Dan Chen

Authors
  1. Dan Chen
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Correspondence to Dan Chen.

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The authors declare that they have no conflict of interest.

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All data generated or analyzed during this study are included in Tables 1 and 2.

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All the codes implemented during this study are available from the corresponding author on reasonable request.

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This paper does not contain any studies with human participants or animals performed by any of the authors.

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Chen, D. Uncertain regression model with autoregressive time series errors. Soft Comput 25, 14549–14559 (2021). https://doi.org/10.1007/s00500-021-06362-4

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  • DOI: https://doi.org/10.1007/s00500-021-06362-4

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