Cited By
View all- Orang Ode Lima e Silva PGuimarães F(2023)Time series forecasting using fuzzy cognitive maps: a surveyArtificial Intelligence Review10.1007/s10462-022-10319-w56:8(7733-7794)Online publication date: 1-Aug-2023
Adaptive online time series prediction based on a novel dynamic fuzzy cognitive map
Pages 5291 - 5303
Abstract
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.
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Published: 01 January 2019
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View all- Orang Ode Lima e Silva PGuimarães F(2023)Time series forecasting using fuzzy cognitive maps: a surveyArtificial Intelligence Review10.1007/s10462-022-10319-w56:8(7733-7794)Online publication date: 1-Aug-2023
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