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

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 th...

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Bibliographic Details
Published inNeural computing & applications Vol. 36; no. 9; pp. 4623 - 4642
Main Authors Yu, Tianming, Li, Qianxin, Wang, Ying, Feng, Guoliang
Format Journal Article
LanguageEnglish
Published London Springer London 01.03.2024
Springer Nature B.V
Subjects
Accuracy
Algorithms
Artificial Intelligence
Automation
Cognitive maps
Cognitive models
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computing time
Data Mining and Knowledge Discovery
Efficiency
Electric power
Electricity distribution
Forecasting
Fuzzy logic
Fuzzy sets
Image Processing and Computer Vision
Mathematical models
Methods
Neural networks
Noise prediction
Numerical prediction
Original Article
Probability and Statistics in Computer Science
Semantics
Time series
Granular computing
Fuzzy cognitive maps
Interval-valued prediction
The least squares method
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-09290-6