Stock Price Prediction using GRU and Dashboards

Abstract

This research focuses on predicting stock prices using Gated Recurrent Units (GRUs), a type of Recurrent Neural Network (RNN) that effectively captures sequential dependencies in time series data. The model leverages historical stock data and presents results using interactive dashboards in Power BI, enhancing decision-making and interpretability for stakeholders. The study evaluates the accuracy and efficiency of GRU-based models against traditional approaches, demonstrating improved forecasting capabilities. Additionally, the research explores the impact of various hyper-parameters on model performance and compares GRU with other deep learning architectures, such as LSTMs, to determine the optimal approach for financial time series forecasting. The system\'s ability to detect trends, mitigate noise, and provide real-time insights makes it a valuable tool for investors and financial analysts. Furthermore, this study examines real-world applications, industry adoption, and the scalability of GRU-based predictive models in financial markets, ensuring robust performance across varying market conditions.

Introduction

Stock price prediction is vital for making informed financial decisions, but the volatile and complex nature of stock markets makes accurate forecasting challenging. Traditional statistical methods like ARIMA and Exponential Smoothing, which assume linearity, often fail to capture the nonlinear and dynamic patterns of stock prices. Advanced machine learning, especially deep learning models such as Gated Recurrent Units (GRUs), offers improved performance by efficiently modeling temporal dependencies in sequential data, overcoming limitations like the vanishing gradient problem in basic RNNs.

This research integrates GRU-based forecasting with Power BI to provide real-time, interactive dashboards for enhanced visualization and analysis. This approach supports investors and analysts in understanding stock trends, detecting market patterns, and optimizing investment strategies with greater transparency.

Traditional methods and basic machine learning models face drawbacks such as limited ability to capture complex market relationships, poor handling of volatility, and lack of real-time processing. The proposed system addresses these issues by combining GRU’s deep learning strengths with Power BI’s visualization capabilities.

The methodology involves data preprocessing, feature engineering (e.g., SMA, RSI), GRU model execution for trend prediction, and real-time dashboard visualization. Key advantages include privacy-preserving AI training, automated trend detection, real-time processing, and improved decision-making support.

Conclusion

The proposed stock price prediction system using Gated Recurrent Units (GRUs) significantly improves upon traditional forecasting methods. By combining deep learning with real-time visualization through Power BI, the system enhances prediction accuracy and reliability. The GRU effectively captures complex temporal dependencies, outperforming conventional statistical models that struggle with non-linear patterns. This research highlights the value of incorporating external factors, such as market sentiment and economic indicators, into predictive models. The GRU’s capability to process sequential data allows for a better understanding of stock price movements, aiding investors in making informed decisions. Additionally, real-time processing ensures access to current market information, supporting timely investment strategies.Interactive dashboards provide a user-friendly interface for visualizing trends and assessing risks, thereby enhancing decision-making. Future work should focus on optimizing the GRU model and exploring additional financial indicators. Overall, this research contributes to financial forecasting, offering a solid framework for future advancements.

References

[1] IEEE Xplore. \"Stock Price Prediction using LSTM and GRU.\" [2] ResearchGate. \"Machine Learning Techniques for Financial Forecasting.\" [3] International Journal of Advanced Computer Science and Applications. \"Comparative Analysis of GRU and LSTM for Stock Market Prediction.\" [4] IEEE Xplore. \"Deep Learning Models for Financial Time Series Analysis.\" [5] Financial Data Analysis Reports. \"Predicting Stock Market Trends using Neural Networks.\" [6] G. M. Applied Machine Learning, 1st Edition, McGraw-Hill, 2018.

Copyright

Copyright © 2025 M Pallavi, Anjali Koduru, Dedeepya Ande, Neyyila Navaneeth Kumar, Kola Swarnalatha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.