Affiliations: College of Mathematics and Statistics, Northwest Normal University, Lanzhou, PR China
Correspondence:
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Corresponding author. Hong Yang, College of Mathematics and Statistics, Northwest Normal University, Lanzhou, PR China. Tel.: +8613619316908. E-mail: [email protected].
Note: [1] This work is supported by National Natural Science Foundation of China (12161082) and Natural Science Foundation of Gansu Province (21JR7RA134).
Abstract: The paper focuses on how to improve the prediction accuracy of time series and the interpretability of prediction results. First, a novel Prophet model based on Gaussian linear fuzzy approximate representation (GF-Prophet) is proposed for long-term prediction, which uniformly predicts the data with consistent trend characteristics. By taking Gaussian linear fuzzy information granules as inputs and outputs, GF-Prophet predicts with significantly smaller cumulative error. Second, noticing that trend extraction affects prediction accuracy seriously, a novel granulation modification algorithm is proposed to merge adjacent information granules that do not have significant differences. This is the first attempt to establish Prophet based on fuzzy information granules to predict trend characteristics. Experiments on public datasets show that the introduction of Gaussian linear fuzzy information granules significantly improves prediction performance of traditional Prophet model. Compared with other classical models, GF-Prophet has not only higher prediction accuracy, but also better interpretability, which can clearly give the change information, fluctuation amplitude and duration of a certain trend in the future that investors actually pay attention to.
Keywords: Fuzzy number, gaussian linear fuzzy information granule, the prophet model, long-term prediction
