A two-stage model for stock price prediction based on variational mode decomposition and ensemble machine learning method

Accurate stock price prediction is critical for investment decisions in the stock market. To improve the performance of stock price prediction, this paper proposes a novel two-stage prediction model that consists of a decomposition algorithm, a nonlinear ensemble strategy, and three individual machine learning models. Specifically, in the first stage, the stock price time series is decomposed into a finite number of sub-series by variational mode decomposition (VMD). Subsequently, three individual machine learning models, namely support vector machine regression (SVR), extreme learning machine (ELM), and deep neural network (DNN), are separately employed to predict decomposed sub-series, and then the obtained sub-series predictions of each individual prediction model are aggregated to generate the preliminary stock price predictions. In the second stage, an ELM-based nonlinear ensemble strategy is employed to combine preliminary stock price predictions. To verify the effectiveness of the proposed two-stage model, it is compared with a total of fourteen models in terms of accuracy evaluation, improvement percentage comparison, and statistical test. The empirical results demonstrate that the proposed two-stage model can obtain better performance than other competitor models.