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. Manuscript profile

Today, different markets and economic sectors are directly or indirectly affected by gold price; thus its prediction is a big challenge for both investors and researchers. On the other hand, the nonstationary and nonlinear patterns of gold price data cause the prediction process even more complex. To address this challenge, a hybrid model was developed in this paper to predict gold price, with a concentration on enhancing accuracy through considering the gold price data characteristics. To do this, Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Gated recurrent units (GRU) were used to deal with the nonstationary and nonlinear nature of the gold price data. The former was first applied to the decomposition of time-series data of gold price into a number of components. Then, GRU 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. The efficiency of the developed model was evaluated using real-world gold data, which confirmed its superiority over the standard methods used for comparison. Manuscript profile