Cover Image

Artificial neural network-salp-swarm algorithm for stock price prediction

Predicting stock prices is a challenging task due to the numerous factors that impact them. The dataset used for analyzing stock prices often displays complex patterns and high volatility, making the generation of accurate predictions difficult. To address these challenge...

Full description

Saved in:
Bibliographic Details
Main Authors: Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Azlan, Abdul Aziz
Format: Article
Language:English
Published: University of Baghdad-College of Science 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/43532/1/Artificial%20Neural%20Network-Salp-Swarm%20Algorithm%20for%20Stock%20Price%20Prediction.pdf
http://umpir.ump.edu.my/id/eprint/43532/
https://doi.org/10.24996/ijs.2024.65.12.34
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.43532
record_format eprints
spelling my.ump.umpir.435322025-01-09T00:51:16Z http://umpir.ump.edu.my/id/eprint/43532/ Artificial neural network-salp-swarm algorithm for stock price prediction Zuriani, Mustaffa Mohd Herwan, Sulaiman Azlan, Abdul Aziz QA75 Electronic computers. Computer science Predicting stock prices is a challenging task due to the numerous factors that impact them. The dataset used for analyzing stock prices often displays complex patterns and high volatility, making the generation of accurate predictions difficult. To address these challenges, this study proposes a hybrid prediction model that combines the salp-swarm algorithm and the artificial neural network (SSA-ANN). The SSA is used to optimize the weights and biases in the ANN, resulting in more reliable and accurate predictions. Before training, the dataset is normalized using the min-max normalization technique to reduce the influence of noise. The effectiveness of the SSA-ANN model is evaluated using the Yahoo stock price dataset. The results show that the SSA-ANN model outperforms other models when applied to normalized data. Additionally, the SSA-ANN model is compared with other two hybrid models: the ANN optimized by the Whale Optimization Algorithm (WOA-ANN) and Moth-Flame Optimizer (MOA-ANN), as well as a single model, namely the Autoregressive Integrated Moving Average (ARIMA). The study’s findings indicate that the SSA-ANN model performs better in predicting the dataset based on the evaluation criteria used. University of Baghdad-College of Science 2024 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/43532/1/Artificial%20Neural%20Network-Salp-Swarm%20Algorithm%20for%20Stock%20Price%20Prediction.pdf Zuriani, Mustaffa and Mohd Herwan, Sulaiman and Azlan, Abdul Aziz (2024) Artificial neural network-salp-swarm algorithm for stock price prediction. Iraqi Journal of Science, 65 (12). pp. 7207-7219. ISSN 0067-2904. (Published) https://doi.org/10.24996/ijs.2024.65.12.34 10.24996/ijs.2024.65.12.34
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Zuriani, Mustaffa
Mohd Herwan, Sulaiman
Azlan, Abdul Aziz
Artificial neural network-salp-swarm algorithm for stock price prediction
description Predicting stock prices is a challenging task due to the numerous factors that impact them. The dataset used for analyzing stock prices often displays complex patterns and high volatility, making the generation of accurate predictions difficult. To address these challenges, this study proposes a hybrid prediction model that combines the salp-swarm algorithm and the artificial neural network (SSA-ANN). The SSA is used to optimize the weights and biases in the ANN, resulting in more reliable and accurate predictions. Before training, the dataset is normalized using the min-max normalization technique to reduce the influence of noise. The effectiveness of the SSA-ANN model is evaluated using the Yahoo stock price dataset. The results show that the SSA-ANN model outperforms other models when applied to normalized data. Additionally, the SSA-ANN model is compared with other two hybrid models: the ANN optimized by the Whale Optimization Algorithm (WOA-ANN) and Moth-Flame Optimizer (MOA-ANN), as well as a single model, namely the Autoregressive Integrated Moving Average (ARIMA). The study’s findings indicate that the SSA-ANN model performs better in predicting the dataset based on the evaluation criteria used.
format Article
author Zuriani, Mustaffa
Mohd Herwan, Sulaiman
Azlan, Abdul Aziz
author_facet Zuriani, Mustaffa
Mohd Herwan, Sulaiman
Azlan, Abdul Aziz
author_sort Zuriani, Mustaffa
title Artificial neural network-salp-swarm algorithm for stock price prediction
title_short Artificial neural network-salp-swarm algorithm for stock price prediction
title_full Artificial neural network-salp-swarm algorithm for stock price prediction
title_fullStr Artificial neural network-salp-swarm algorithm for stock price prediction
title_full_unstemmed Artificial neural network-salp-swarm algorithm for stock price prediction
title_sort artificial neural network-salp-swarm algorithm for stock price prediction
publisher University of Baghdad-College of Science
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/43532/1/Artificial%20Neural%20Network-Salp-Swarm%20Algorithm%20for%20Stock%20Price%20Prediction.pdf
http://umpir.ump.edu.my/id/eprint/43532/
https://doi.org/10.24996/ijs.2024.65.12.34
_version_ 1822924930272985088
score 13.232414

Similar Items