Forecasting multivariate time series is an important problem considered in many real-world scenarios. To deal with that problem, several forecasting models have already been proposed, where Fuzzy Cognitive Maps (FCMs) are proved to be a suitable alternative. The key limitation of the existing FCM-based forecasting models is the lack of time-efficient learning algorithms. In this paper, we plug that gap by proposing a new FCM learning algorithm which is based on Moore–Penrose inverse. Moreover, we propose an innovative approach that equips FCM with long-term, multistep prediction capabilities. A huge advantage of our method is the lack of parameters which in the case of competitive approaches require laborious adjustment or tuning. The other added value of our method is the reduction of the processing time required to train FCM. The performed experiments revealed that FCM trained using our method outperforms the best FCM-based forecasting model reported in the literature.

预测多元时间序列是许多现实世界中考虑的重要问题。为了解决该问题，已经提出了几种预测模型，其中模糊认知图（FCM）被证明是合适的选择。现有基于FCM的预测模型的主要局限性在于缺乏高效的学习算法。在本文中，我们通过提出一种新的基于Moore-Penrose逆的FCM学习算法来填补这一空白。此外，我们提出了一种创新的方法，使FCM具有长期的多步预测功能。我们的方法的一个巨大优点是缺少参数，在竞争方法的情况下，这些参数需要费力的调整或调整。我们方法的另一个附加价值是减少训练FCM所需的处理时间。