Forecasting neural network model with novel CID learning rate and EEMD algorithms on energy market

In view of the applications of artificial neural networks in economic and financial forecasting, it is vital to improve the prediction methods and the forecasting accuracy for the neural networks. In this paper, a neural network architecture with novel learning rate which is controlled by the complexity invariant distance (CID) is developed for energy market forecasting, where the CID is generally utilized to measure the complexity differences between two time series by employing the Euclidean distance. Moreover, stochastic time strength neural network (STNN) is a kind of supervised neural network which is introduced to forecast the time series. Based on the above theories, a new neural network model called CID-STNN is proposed in this work, in an attempt to improve the forecasting accuracy. For comparing the forecasting performance of CID-STNN and STNN deeply, the ensemble empirical mode decomposition (EEMD) is applied to decompose time series into several intrinsic mode functions (IMFs), and these IMFs are utilized to train the models. Further, the empirical research is performed in testing the prediction effect of WTI and Brent by evaluating predicting ability of the proposed model, and the corresponding superiority is also demonstrated. (C) 2018 Elsevier B.V. All rights reserved.