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- DOI: 10.12720/jait
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Home > Published Issues > 2023 > Volume 14, No. 6, 2023 >
JAIT 2023 Vol.14(6): 1254-1260
doi: 10.12720/jait.14.6.1254-1260
doi: 10.12720/jait.14.6.1254-1260
Gradient Boosting and LSTM Based Hybrid Ensemble Learning for Two Step Prediction of Stock Market
Pratyush Ranjan Mohapatra 1, Ajaya Kumar Parida 1,*, Santosh Kumar Swain 1, and
Santi Swarup Basa 2
1. School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, Odisha, India; Email: pratyush.mohapatra@gmail.com (P.R.M.), sswainfcs@kiit.ac.in (S.K.S.)
2. Department of Computer Science, Maharaja Sriram Chandra Bhanjadeo University, Baripada, Odisha, India;
Email: santiswarup.basa@gmail.com (S.S.B.)
*Correspondence: ajaya.paridafcs@kiit.ac.in (A.K.P.)
2. Department of Computer Science, Maharaja Sriram Chandra Bhanjadeo University, Baripada, Odisha, India;
Email: santiswarup.basa@gmail.com (S.S.B.)
*Correspondence: ajaya.paridafcs@kiit.ac.in (A.K.P.)
Manuscript received March 2, 2023; revised March 21, 2023; accepted May 30, 2023; published November 22, 2023.
Abstract—Prediction of stock market price using different artificial intelligent techniques have become an efficient and effective method for stock market prediction with higher prediction accuracy. In this present work, thus we provide an ensemble technique that comprises of two base models namely extreme gradient boosting method and long short term memory method for short term prediction of stock market. Previously the prediction of stock price was confined to all the data available, irrespective of its significance in prediction accuracy. This study investigates different issues for predicting the closing price of the stock market. Based on the two step ensemble method (including a feature selection and combination of two different intelligent techniques). Convolutional Neural Network (CNN) method is used for feature selection purpose based on the correlation coefficient of different technical indicators for predicting the closing price. Additionally, ensemble learning is applied for increasing the prediction accuracy. The subset of selected input features enhances the model’s accuracy. The performance evaluation of the proposed model is performed by comparing it with different other models like Support Vector Machine (SVM), Long Short Term Memory (LSTM), Kernel Extreme Learning Machine (KELM), etc. As a new addition to the previous literature the proposed combined method extracts the features that mainly influences the accuracy of the predicted price hence better result in less time is observed. The proposed ensemble learning technique exhibited the best predicted output as compared with other methods discussed in this study.
Keywords—artificial intelligence, technical indicators, ensemble learning, Extreme Gradient Boosting (XGB)
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
Keywords—artificial intelligence, technical indicators, ensemble learning, Extreme Gradient Boosting (XGB)
Cite: Pratyush Ranjan Mohapatra, Ajaya Kumar Parida, Santosh Kumar Swain, and Santi Swarup Basa, "Gradient Boosting and LSTM Based Hybrid Ensemble Learning for Two Step Prediction of Stock Market," Journal of Advances in Information Technology, Vol. 14, No. 6, pp. 1254-1260, 2023.
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
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