Time Series Prediction Using Sparse Autoencoder and High-Order Fuzzy Cognitive Maps

The problem of time series prediction based on fuzzy cognitive maps (FCMs) is unresolved. Although many methods have been proposed to cope with this issue, the performance of these methods is far from satisfactory. Traditional FCM-based predictors have three limitations. First, current feature extra...

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Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 28; H. 12; S. 3110 - 3121
Hauptverfasser:	Wu, Kai, Liu, Jing, Liu, Penghui, Yang, Shanchao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Cognitive maps Cognitive models Computational modeling Feature extraction Fuzzy cognitive maps Fuzzy cognitive maps (FCMs) fuzzy time series Machine learning Numerical models Prediction algorithms Prediction models Predictive models sparse autoencoder (SAE) Time series Time series analysis time series prediction
ISSN:	1063-6706, 1941-0034
Online-Zugang:	Volltext
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