Stock market prediction using hybrid multi-layer decomposition and optimized multi-kernel extreme learning machine

The financial time series data is a highly nonlinear signal and hence difficult to predict precisely. The prediction accuracy can be improved by linearizing the signal. In this paper the nonlinear data sample is linearized by decomposing it into several IMFs. A hybrid multi-layer decomposition technique is developed. The decomposition proposed in this paper is the combination of both EMD and VMD methods. As a new contribution to the previous literature in this study the VMD is used to further decompose the higher frequency signals obtained from the EMD based decomposed signal. In the result analysis it is observed that the double decomposition improves the prediction accuracy. This is a new introduction in the field of stock market prediction. The prediction accuracy of the proposed model is performed by applying it to three different stock markets for predicting the closing price. Historical data (closing price) is implemented to obtain 1 day ahead predicted closing price. Comparative analysis of different previously implemented methods like BPNN, SVM, ANN and ELM, along with the proposed method is performed. GA is implemented for optimizing the kernel factors. It is observed that the proposed hybrid model outperformed the other methods.