Enhancing Stock Price Prediction Accuracy Using ARIMA and Advanced Greylag Goose Optimizer Algorithm
Abotaleb, M., Lim, W., Mishra, P., Qenawy, A., ALmetwally, E. (2024). Enhancing Stock Price Prediction Accuracy Using ARIMA and Advanced Greylag Goose Optimizer Algorithm. EKB Journal Management System, 1(1), 49-65. doi: 10.21608/jaiep.2024.355004
Mostafa Abotaleb; Wei Hong Lim; Pradeep Mishra; Al-Seyday.T. Qenawy; Ehab M. ALmetwally. "Enhancing Stock Price Prediction Accuracy Using ARIMA and Advanced Greylag Goose Optimizer Algorithm". EKB Journal Management System, 1, 1, 2024, 49-65. doi: 10.21608/jaiep.2024.355004
Abotaleb, M., Lim, W., Mishra, P., Qenawy, A., ALmetwally, E. (2024). 'Enhancing Stock Price Prediction Accuracy Using ARIMA and Advanced Greylag Goose Optimizer Algorithm', EKB Journal Management System, 1(1), pp. 49-65. doi: 10.21608/jaiep.2024.355004
Abotaleb, M., Lim, W., Mishra, P., Qenawy, A., ALmetwally, E. Enhancing Stock Price Prediction Accuracy Using ARIMA and Advanced Greylag Goose Optimizer Algorithm. EKB Journal Management System, 2024; 1(1): 49-65. doi: 10.21608/jaiep.2024.355004

Enhancing Stock Price Prediction Accuracy Using ARIMA and Advanced Greylag Goose Optimizer Algorithm

Journal of Artificial Intelligence in Engineering Practice
Volume 1, Issue 1, April 2024, Page 49-65  XML PDF (1.62 MB)
Document Type: Original Article
DOI: 10.21608/jaiep.2024.355004
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Authors
Mostafa Abotaleb email 1; Wei Hong Lim2; Pradeep Mishra3; Al-Seyday.T. Qenawy4; Ehab M. ALmetwallyorcid 5
1Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia
2Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia
3College of Agriculture, Jawaharlal Nehru Krishi Vishwa Vidyalaya (JNKVV), Rewa 486001, India
4Intelligent Systems and Machine Learning Lab, Shenzhen 518000, China
5Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
Abstract
This paper applies ARIMA and the Greylag Goose Optimizer (GGO) algorithm, among others, for pre-trending the stock market prediction. This study aims to improve stock price forecasting using proper feature selection methods and time series modeling. The dataset encompasses historical pricing data that was scraped from Yahoo Finance in January 2019. There were many models for feature selection applied in the data preprocessing phase, such as bGGO, bGWO, bGWO_PSO, bPSO, bWAO, bBBO, bMVO, bSBO, bGWOGA and bFA. The model's performance was judged on metrics based on average error, select size and fitness; the GGO (Greylag Goose Optimizer) algorithm had the best performance rating, with an Average error equal to 0.36455. The ARIMA model was used to predict future stock prices based on the selected features, with the lowest MSE being 0.001367831 and an RMSE of 0.0369842, evidencing powerful forecasting ability. This outcome proves the reliability of integrating feature selection approaches with ARIMA for stock price prediction; it helps improve forecast accuracy and provides valuable insights for investors and analysts. The final point indicates the importance of powerful optimization tools in decision-making.
Keywords
Stock Prices; Feature Selection; ARIMA; Greylag Goose Optimizer; Financial Data; Machine Learning
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