Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey

TL;DR

This survey reviews the use of Fuzzy Cognitive Maps (FCM) for time series forecasting, highlighting recent models, fundamentals, and future research directions to improve scalability, learning speed, and handling non-stationary data.

Contribution

It provides a comprehensive overview of FCM-based forecasting models, fundamentals, and suggests future research avenues for enhancing FCM capabilities.

Findings

FCM models effectively capture complex system dynamics.

Recent models improve interpretability and learning capabilities.

Future directions include addressing non-stationarity and scalability.

Abstract

Among various soft computing approaches for time series forecasting, Fuzzy Cognitive Maps (FCM) have shown remarkable results as a tool to model and analyze the dynamics of complex systems. FCM 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.