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Interval-valued prediction of time series based on fuzzy cognitive maps and granular computing

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Abstract

Time series have yielded impressive results in numerical prediction, yet the presence of noise can significantly affect accuracy. Although interval prediction can minimize noise interference, most methods only predict upper and lower limits separately, resulting in uninterpretable predictions. In this paper, we propose a novel modeling approach for time-series interval prediction that integrates granular computing and fuzzy cognitive maps (FCMs). Granular computing transforms traditional numerical time series into interval time series. Rather than predicting interval values independently, our method mines the fuzzy relationship between information granules to obtain the affiliation matrix. During the prediction stage, an FCM-based model is established to predict the affiliation matrix. We conducted experiments on six publicly available datasets, and results demonstrate that our method reduces the impact of noise while offering improved interpretability for prediction outcomes. More importantly, our approach yields significantly lower interval prediction errors when compared to other advanced methods.

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All data generated or analyzed during this study are included in this published article. Derived data supporting the findings of this study are available from the corresponding author on request.

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Acknowledgements

This work was supported by the Research Initiation Project of Northeast Electric Power University 12081 and the Science and Technology Project of Jilin Province under Grant 20230101240JC.

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Authors and Affiliations

  1. College of Automation Engineering, Northeast Electric Power University, Changchun Street, Jilin City, 132012, Jilin, China

    Tianming Yu & Guoliang Feng

  2. College of Information and Control Engineering, Jilin Institute of Chemical Technology, Chengde Street, Jilin City, 100190, Jilin, China

    Qianxin Li

  3. Economic and Technological Research Institute, State Grid Heilongjiang Electric Power Co., Ltd., Changjiang Road, Harbin, 610101, Heilongjiang, China

    Ying Wang

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  1. Tianming Yu
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Correspondence to Guoliang Feng.

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Yu, T., Li, Q., Wang, Y. et al. Interval-valued prediction of time series based on fuzzy cognitive maps and granular computing. Neural Comput & Applic 36, 4623–4642 (2024). https://doi.org/10.1007/s00521-023-09290-6

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