Abstract
The paper focuses on the application of fuzzy cognitive map (FCM) with multi-step learning algorithms based on gradient method and Markov model of gradient for prediction tasks. Two datasets were selected for the implementation of the algorithms: real data of household electricity consumption and stock exchange returns that include Istanbul Stock Exchange returns. These data were used in learning and testing processes of the proposed FCM approaches. A comparative analysis of the two-step method of Markov model of gradient, multi-step gradient method and one-step gradient method is performed in order to show the capabilities and effectiveness of each method and conclusions are based on the obtained MSE, RMSE, MAE and MAPE errors.
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Acknowledgments
Elpiniki I. Papageorgiou acknowledge the support of the ERC08- RECITAL project, co-financed by Greece and the European Social Fund through the Education and Lifelong Learning Operational Program of the Greek National Strategic Reference Framework 2007–2013.
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Papageorgiou, E., Poczęta, K., Yastrebov, A., Laspidou, C. (2015). Fuzzy Cognitive Maps and Multi-step Gradient Methods for Prediction: Applications to Electricity Consumption and Stock Exchange Returns. In: Neves-Silva, R., Jain, L., Howlett, R. (eds) Intelligent Decision Technologies. IDT 2017. Smart Innovation, Systems and Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-19857-6_43
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