Stochastic Financial Price Deep Learning Forecasting Model And Statistical Analysis

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 en-ergy 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 neu-ral 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.In recent years,with the development of artificial intelligence,deep learning has attracted wide attention in various industrial fields.Some scientific research about using the deep learning model to fit and predict time series has been developed.In an attempt to increase the accura-cy of oil market price prediction,Long Short Term Memory,a representative model of deep learning,is applied to fit crude oil prices in this paper.In the traditional application field of Long Short Term Memory,such as natural language processing,large amount of data is a consensus to improve training accuracy of Long Short Term Memory.In order to improve the prediction accuracy by extending the size of training set,trans-fer learning provides a heuristic data extension approach.Moreover,considering the equivalent of each historical data to train the Long Short Term Memory is difficult to reflect the changeable behaviors of crude oil markets,a very creative algorithm named data transfer with prior knowledge which provides a more availability data extension approach is proposed in this paper.For comparing the predicting performance of initial data and data transfer deeply,the ensemble empirical mode decomposition is applied to decompose time series into several intrinsic mode functions,and these intrinsic mode functions are utilized to train the models.Further,the empirical research is performed in testing the prediction effect of West Texas Intermediate and Brent crude oil by evalu-ating predicting ability of the proposed model,and the corresponding superiority is also demonstrated.