Forecasting Social Security Revenues in Jordan Using Fuzzy Cognitive Maps

Ahmad Zyad Alghzawi*, Gonzalo Nápoles, George Sammour, Koen Vanhoof

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publicationIntelligent Decision Technologies 2017
EditorsIreneusz Czarnowski, Robert J. Howlett, Lakhmi C. Jain
Place of PublicationCham
PublisherSpringer International Publishing
Pages246-254
Number of pages9
ISBN (Print)978-3-319-59421-7
Publication statusPublished - 2018
Externally publishedYes
Event9th KES International Conference on Intelligent Decision Technologies (KES-IDT 2017) -
Duration: 21 Jun 2017 → …

Conference

Conference9th KES International Conference on Intelligent Decision Technologies (KES-IDT 2017)
Period21/06/17 → …

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