Regular Issue - 2
Because of the inherent volatility and complexity of the financial markets, accurately predicting stock prices is a challenging task. By assessing the performance of many predictive models, including Random Forest, Stacked LSTM, Stacked GRU, ARIMA (Auto Regression Integrated Moving Average), network with long short-term memory (LSTM) and support vector machine (SVM), the study seeks to tackle this challenge. We thoroughly evaluate these model’s performance by looking at their capacity for generalization and prediction accuracy. The GRU model outperformed the LSTM model with a substantially lower validation loss of 0.0101 and a training loss of 0.0067, whereas the LSTM model obtained a validation loss 0.5048 with a training loss of 0.0130. The SVM with Support Vector regression (SVR) demonstrated a much larger Mean Squared Error (MSE) of 2878.20, whereas Random Forest model produced an MSE of 85.57. The GRU and LSTM models were further improved by the stacked architectures; the Stacked GRU achieved loss of 0.0361 and the Stacked LSTM achieved a validation loss of 0.9658. Despite being simpler, the ARIMA model yielded a Mean Squared Error of 11.8594. The results of this through investigation show that the GRU and Stacked GRU models perform the best
100
75
43
100
75
43
100
75
43
India
"""
Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Hari Krishna, yenugondaharikrishna@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
No. of Readers
No. of Reaction
No. of Comments
No. of Downloads
Or share your Opinion
India
India
India
The increasing prevalence of Internet of Things (IoT) devices in various domains underscores the necessity for robust security...
The rapid advancement in processing power has empowered deep learning algorithms to produce remarkably convincing human-synthesized videos, commonly...
Automated detection and diagnosis of pulmonary diseases such as pneumonia, tuberculosis, lung cancer, and COVID-19 play a crucial...
The goal of this project is to develop a sophisticated and intelligent system that tracks soil moisture levels...
The By employing a machine learning algorithm to analyse traffic and identify intrusions, intrusion detection systems assist in...
Impact Factor
Scopus & Web of Science under Evalution
Comments(0)