Stock price series forecasting using multi-scale modeling with boruta feature selection and adaptive denoising

Abstract

In recent times, predicting stock prices has garnered attention from both regulators and academic circles. However, the intricate nature of financial time-series data, with its nonlinearities, discontinuities, and sensitivity to noise, complicates the understanding and forecasting of financial movements. In our approach, we initially deploy an adaptive empirical modal decomposition on the primary data to enhance model precision. Subsequently, we sift the technical indicator data through the Boruta method, enhancing selected functionalities via an adaptive noise reduction technique. We then employ support vector regression (SVR) integrated with brain storm optimization algorithm (BSO) for effective data handling and forecasting target variables. Our results suggest that the composite model outlined in this paper outperforms the other eight comparison models in terms of reducing errors and improving regression scores. Additionally, when juxtaposed against these four models, the outcomes reinforce the efficiency of our proposed multiscale strategy and denoising technique in refining prediction accuracy.