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Time series forecasting using fuzzy cognitive maps: a survey

Artificial Intelligence Review (2022)Cite this article

Abstract

Among various soft computing approaches for time series forecasting, fuzzy cognitive maps (FCMs) have shown remarkable results as a tool to model and analyze the dynamics of complex systems. FCMs have similarities to recurrent neural networks and can be classified as a neuro-fuzzy method. In other words, FCMs are a mixture of fuzzy logic, neural network, and expert system aspects, which act as a powerful tool for simulating and studying the dynamic behavior of complex systems. The most interesting features are knowledge interpretability, dynamic characteristics and learning capability. The goal of this survey paper is mainly to present an overview on the most relevant and recent FCM-based time series forecasting models proposed in the literature. In addition, this article considers an introduction on the fundamentals of FCM model and learning methodologies. Also, this survey provides some ideas for future research to enhance the capabilities of FCM in order to cover some challenges in the real-world experiments such as handling non-stationary data and scalability issues. Moreover, equipping FCMs with fast learning algorithms is one of the major concerns in this area.

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Fig. 1

Notes

  1. Notice that in order to compute the effect of the other concepts \(c_{j}\ne c_{i}\) into \(c_{i}\), we need to perform the summation of terms \(w_{ji}a_{j}(t)\), given that \(w_{ji}\) reflects the causal connection between \(c_{j}\) and \(c_{i}\).

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Authors and Affiliations

  1. Machine Intelligence and Data Science (MINDS) Laboratory, Federal University of Minas Gerais, Belo Horizonte, Brazil

    Omid Orang, Petrônio Cândido de Lima e Silva & Frederico Gadelha Guimarães

  2. Federal Institute of Education Science and Technology of Northern Minas Gerais, Januária Campus, Belo Horizonte, Brazil

    Petrônio Cândido de Lima e Silva

  3. Department of Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Frederico Gadelha Guimarães

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  1. Omid Orang
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Orang, O., de Lima e Silva, P.C. & Guimarães, F.G. Time series forecasting using fuzzy cognitive maps: a survey. Artif Intell Rev (2022). https://doi.org/10.1007/s10462-022-10319-w

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Keywords

  • Time series forecasting
  • Fuzzy cognitive maps
  • Soft computing
  • Fuzzy systems