Abstract
Uncertain time series models constitute a pivotal tool for analyzing phenomena evolving over time. However, most of the prevailing research centers on parameter models, which exhibit limitations in addressing intricate temporal dynamics. To address this gap, this paper introduces a non-parametric uncertain time series model. Primarily, we propose the definition of the non-parametric uncertain time series models. Subsequently, tailored to the non-parametric uncertain additive autoregressive model, we propose a non-parametric estimation method based on linear polynomial splines. Following this, the effectiveness of these estimations is verified through residual analysis and uncertain hypothesis tests. Finally, we apply the introduced non-parametric estimation approach to the Brent crude oil spot price.
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This work was supported by the National Natural Science Foundation of China [grant number 72201022].
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Zhang, Y., Gao, J. Nonparametric uncertain time series models: theory and application in brent crude oil spot price analysis. Fuzzy Optim Decis Making 23, 239–252 (2024). https://doi.org/10.1007/s10700-024-09419-9
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DOI: https://doi.org/10.1007/s10700-024-09419-9