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 | Source: | 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. | Notes: | Alghzawi, AZ (reprint author), Hasselt Univ, Dept Business Informat, Hasselt, Belgium. ahmad.alghzawi@uhasselt.be | Keywords: | fuzzy Cognitive Maps; time series prediction; economic modeling | Document URI: | http://hdl.handle.net/1942/25664 | ISBN: | 9783319594200 | DOI: | 10.1007/978-3-319-59421-7_23 | ISI #: | 000432721700023 | Rights: | © Springer International Publishing AG 2018 | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2019 |
Appears in Collections: | Research publications |
Show full item record
SCOPUSTM
Citations
1
checked on Sep 3, 2020
WEB OF SCIENCETM
Citations
3
checked on Oct 14, 2024
Page view(s)
72
checked on Sep 7, 2022
Download(s)
38
checked on Sep 7, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.