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An Ensemble of Random Forest Gradient Boosting Machine and Deep Learning Methods for Stock Price Prediction

An Ensemble of Random Forest Gradient Boosting Machine and Deep Learning Methods for Stock Price Prediction

Lokesh Kumar Shrivastav, Ravinder Kumar
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 19
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.2022010102
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MLA

Shrivastav, Lokesh Kumar, and Ravinder Kumar. "An Ensemble of Random Forest Gradient Boosting Machine and Deep Learning Methods for Stock Price Prediction." JITR vol.15, no.1 2022: pp.1-19. http://doi.org/10.4018/JITR.2022010102

APA

Shrivastav, L. K. & Kumar, R. (2022). An Ensemble of Random Forest Gradient Boosting Machine and Deep Learning Methods for Stock Price Prediction. Journal of Information Technology Research (JITR), 15(1), 1-19. http://doi.org/10.4018/JITR.2022010102

Chicago

Shrivastav, Lokesh Kumar, and Ravinder Kumar. "An Ensemble of Random Forest Gradient Boosting Machine and Deep Learning Methods for Stock Price Prediction," Journal of Information Technology Research (JITR) 15, no.1: 1-19. http://doi.org/10.4018/JITR.2022010102

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Abstract

Stochastic time series analysis of high-frequency stock market data is a very challenging task for the analysts due to the lack availability of efficient tool and techniques for big data analytics. This has opened the door of opportunities for the developer and researcher to develop intelligent and machine learning based tools and techniques for data analytics. This paper proposed an ensemble for stock market data prediction using three most prominent machine learning based techniques. The stock market dataset with raw data size of 39364 KB with all attributes and processed data size of 11826 KB having 872435 instances. The proposed work implements an ensemble model comprises of Deep Learning, Gradient Boosting Machine (GBM) and distributed Random Forest techniques of data analytics. The performance results of the ensemble model are compared with each of the individual methods i.e. deep learning, Gradient Boosting Machine (GBM) and Random Forest. The ensemble model performs better and achieves the highest accuracy of 0.99 and lowest error (RMSE) of 0.1.