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.
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Acknowledgments
We would like to thank our colleague Frank Vanhoenshoven for his valuable support and constructive comments.
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Alghzawi, A.Z., Nápoles, G., Sammour, G., Vanhoof, K. (2018). Forecasting Social Security Revenues in Jordan Using Fuzzy Cognitive Maps. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-59421-7_23
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DOI: https://doi.org/10.1007/978-3-319-59421-7_23
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