Analysis and prediction of confirmed COVID-19 cases in China with uncertain time series

Tingqing Ye () and Xiangfeng Yang ()
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Tingqing Ye: Tsinghua University
Xiangfeng Yang: University of International Business and Economics

Fuzzy Optimization and Decision Making, 2021, vol. 20, issue 2, No 4, 209-228

Abstract: Abstract This paper presents an uncertain time series model to analyse and predict the evolution of confirmed COVID-19 cases in China, excluding imported cases. Compared with the results of the classical time series model, the uncertain time series model could better describe the COVID-19 epidemic by using an uncertain hypothesis test to filter out outliers. This improvement is reflected in the two observations. One is that the estimated variance of the disturbance term in the uncertain time series model is more appropriate and acceptable than that in the classical time series model, and the other is that the disturbance term of the classical time series model cannot be regarded as a random variable but as an uncertain variable.

Keywords: Uncertainty theory; Uncertain time series; Uncertain hypothesis test; COVID-19 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10700-020-09339-4

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