A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps

Fuzzy cognitive maps (FCMs) have been widely used in time series prediction due to the excellent performance in dynamic system modeling. However, existing time series prediction methods based on FCMs have some defects, such as low precision and sensitivity to hyper parameters. Therefore, more accurate and robust methods remain to be proposed for handling non-stationary and large-scale time series. To address this issue, in this paper, a novel time series prediction method based on empirical mode decomposition (EMD) and high-order FCMs (HFCMs) is proposed, termed as EMD-HFCM. First, EMD is applied to extract features from the original sequence to obtain multiple sequences to represent the nodes of HFCM. To learn HFCM efficiently and accurately, a robust learning method based on Bayesian ridge regression is employed, which can estimate the regular parameters from data instead of being set manually. Then, prediction can be performed based on the iterative characteristics of HFCM. To compare EMD-HFCM with existing methods, extensive experiments are conducted on eight benchmark datasets and the results validate the performance of the proposal in handling large-scale and non-stationary time series. Furthermore, the experiments also show that the proposed method is much more robust and insensitive to hyper parameters than the state of art methods. Finally, nonparametric statistical tests are carried out and the superiority of the proposed method is verified in the statistical sense. (C) 2020 Elsevier B.V. All rights reserved.