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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/25664

Title: Forecasting social security revenues in Jordan using Fuzzy Cognitive Maps
Authors: Al Ghzawi, Ahmad
Napoles Ruiz, Gonzalo
Sammour, George
Vanhoof, Koen
Issue Date: 2017
Publisher: © Springer International Publishing AG 2018
Citation: Czarnowski, Ireneusz; Howlett, Robert J.; Jain, Lakhmi C. (Ed.). Intelligent Decision Technologies 2017: Proceedings of the 9th KES International Conference on Intelligent Decision Technologies (KES-IDT 2017) – Part I, © Springer International Publishing AG 2018,p. 246-254
Series/Report: Smart Innovation, Systems and Technologies
Series/Report no.: 72
Abstract: In recent years, Fuzzy Cognitive Maps (FCMs) have become a convenient knowledge-based tool for economic modeling. Perhaps, the most attractive feature of these cognitive networks relies on their transparency when performing the reasoning process. For example, in the context of time series forecasting, an FCM-based model allows predicting the next outcomes while expressing the underlying behavior behind the investigated system. In this paper, we investigate the forecasting of social security revenues in Jordan using these neural networks. More specifically, we build an FCM forecasting model to predict the social security revenues in Jordan based on historical records comprising the last 120 months. It should be remarked that we include expert knowledge related to the sign of each weights, whereas the intensity in computed by a supervised learning procedure. This allows empirically exploring a sensitive issue in such models: the trade-off between interpretability and accuracy.
URI: http://hdl.handle.net/1942/25664
DOI: 10.1007/978-3-319-59421-7_23
ISBN: 9783319594200
ISSN: 2190-3018
Category: C1
Type: Proceedings Paper
Appears in Collections: Research publications

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