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Uncertain vector autoregressive model with imprecise observations

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Abstract

Prior uncertain autoregressive (UAR) model research has focused principally on a univariate time series. However, different variables tend to influence each other in reality. In order to fill this gap, this paper explores the interrelationships among different variables and proposes an exposition of uncertain vector autoregressive (UVAR) model. Furthermore, we choose the least squares principle to estimate the unknown parameters in the UVAR model and analyze the residual of disturbance term. Then, we present the point estimation and confidence interval of the variables in the next period. Finally, the empirical results show that essential improvements in forecasting can be obtained by adding relative variables.

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Acknowledgements

This work was supported by National Natural Science Foundation of China Grant No.61873329.

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Correspondence to Han Tang.

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Communicated by V. Loia.

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Tang, H. Uncertain vector autoregressive model with imprecise observations. Soft Comput 24, 17001–17007 (2020). https://doi.org/10.1007/s00500-020-04991-9

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  • DOI: https://doi.org/10.1007/s00500-020-04991-9

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