Adaptive online time series prediction based on a novel dynamic fuzzy cognitive map

This paper proposes a fuzzy cognitive map scheme for real-time online data prediction. The fuzzy cognitive maps (FCMs) are constructed on the basis of abstracting a numerical time series into a limited number of nodes (concepts), and used as a modeling tool for predicting time series. By representin...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2019-01, Vol.36 (6), p.5291-5303
Main Authors: Nannan, Zhang, Chao, Luo
Format: Article
Language:English
Subjects:
Algorithms
Amplitudes
Clustering
Cognitive maps
Cognitive models
Fuzzy sets
Granular materials
Mathematical models
Order parameters
Particle swarm optimization
Predictions
Process parameters
Real time
Search process
Time series
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Summary:This paper proposes a fuzzy cognitive map scheme for real-time online data prediction. The fuzzy cognitive maps (FCMs) are constructed on the basis of abstracting a numerical time series into a limited number of nodes (concepts), and used as a modeling tool for predicting time series. By representing time series in terms of information granules constructed in the space of amplitude and change of amplitude of the time series, a fuzzy cognitive map is dynamically constructed by using the set of information granule, where the particle swarm optimization (PSO) is utilized to study the parameters. In order to find better weights in the global search process, each parameter of the particle swarm algorithm (PSO) is not set to a fixed value but adaptively changes. In this paper, a dynamic fuzzy C-means clustering algorithm is used to online adjust the cluster center and weight according to the impact of the incoming data at the current moment such that the model can capture real-time changes in the data information. The proposed approach is illustrated in detail by a series of experiments using a collection of publicly available data.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-181064