a hybrid model using deep learning to predict stock price index

Predicting the stock price is a demanding task since multiple factors affect it. to enhance the stock price index prediction accuracy, the current study hybridizes variational mode decomposition (vmd) with the cnn-lstm model. the proposed model, vmd-cnn-lstm, works based on the decomposition-and-ensemble framework. to do this, vmd and cnn-lstm were used to deal with the nonstationary and nonlinear nature of the stock price data. the former was first applied to the decomposition of time-series data into a number of components. then, cnn-lstm was applied to the prediction of the components. to end with, all the components’ prediction results were summed up to attain the final prediction result. to verify the effectiveness of the proposed model in terms of predicting the stock price index, its performance was compared to some single models as well as some vmd- and emd-based hybrid models. the results not only confirmed the superiority of the hybrid models over the single ones, but also showed the higher effectiveness of vmd-based models compared to emd-based ones regarding the prediction accuracy.