Robust empirical wavelet fuzzy cognitive map for time series forecasting

Abstract Fuzzy cognitive maps have achieved significant success in time series modeling and forecasting. However, fuzzy cognitive maps still contain weakness to handle the nonstationarity and outliers. We propose a novel time series forecasting model based on fuzzy cognitive maps and empirical wavelet transformation in this paper. The empirical wavelet transformation is applied to decompose the original time series into different levels which capture information of different frequencies. Then, the high-order fuzzy cognitive map is trained to model the relationships among all the sub-series generated and original time series. To enhance the robustness of high-order fuzzy cognitive maps against outliers, a novel learning method based on support vector regression is designed. Finally, we divide the summation of each concept value of the high-order fuzzy cognitive map by two to obtain the numerical predictions. A comprehensive empirical study on eight public time series validates the superiority of proposed model compared with the popular baseline models from the literature.