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Stock price series forecasting using multi-scale modeling with boruta feature selection and adaptive denoising
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 movem...
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Published in: | Applied soft computing 2024-03, Vol.154, p.111365, Article 111365 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | 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.
•Adaptive selection of technical indicators as input features using the Boruta feature selection algorithm.•Adaptive denoising of input features based on the proposed MIC-ICEEMDAN denoising algorithm.•The reconstruction uses the enhanced BSO-SVR model to predict the prediction results of each IMF and residual term Res. |
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ISSN: | 1568-4946 1872-9681 |
DOI: | 10.1016/j.asoc.2024.111365 |