Journal of Computer Science Engineering and Software Testing https://matjournals.net/engineering/index.php/JOCSES <p><strong>JOCSES</strong> is a peer reviewed journal in the discipline of Computer Science published by the MAT Journals Pvt. Ltd. It is a print and e-journal focused towards the rapid publication of fundamental research papers on all areas of Computer Science Engineering and Software Testing. Software Engineering is the study and application of engineering to the design, development, and maintenance of software. Where Software testing is an investigation conducted to provide stakeholders with information about the quality of the product or service under test.</p> en-US Journal of Computer Science Engineering and Software Testing 2581-6969 Deep Learning-Based Bitcoin Price Prediction Using Long Short-Term Memory Networks https://matjournals.net/engineering/index.php/JOCSES/article/view/447 <p>Cryptocurrencies, notably Bitcoin, have surged in popularity and importance, attracting widespread attention due to their volatile nature and potential for significant financial gains. Predicting Bitcoin prices accurately has emerged as a challenging yet indispensable task for investors, traders, and policymakers seeking to navigate the complexities of the cryptocurrency market. Traditional forecasting methods often struggle to capture the intricate temporal patterns inherent in this domain, prompting the exploration of advanced techniques such as deep learning. This paper presents a comprehensive study on employing Long Short-Term Memory (LSTM) networks for Bitcoin price prediction, a variant of Recurrent Neural Networks (RNNs). Our research encompasses the entire process, from data collection to model evaluation, focusing on addressing the unique challenges posed by cryptocurrency market data. We begin by discussing the methodology involved in data acquisition, preprocessing, and feature engineering, which are crucial steps for ensuring the quality and relevance of input data. Next, we delve into the intricacies of LSTM model selection, hyperparameter tuning, and training strategies, aiming to optimize predictive performance while mitigating overfitting. Through extensive experimentation on real-world Bitcoin price datasets spanning multiple periods, we rigorously evaluate the effectiveness of LSTM networks in capturing long-term dependencies and forecasting future price movements. Comparative analyses against traditional time-series forecasting methods underscore the superior predictive capabilities of deep learning approaches in this context.</p> <p>Furthermore, we investigate the interpretability of LSTM-based models, examining the insights gained from analyzing learned representations and hidden states. These analyses provide valuable perspectives for market participants seeking to understand the driving factors behind Bitcoin price dynamics. Our findings demonstrate the practical utility of LSTM networks in Bitcoin price prediction and contribute to advancing the broader discourse on deep learning applications in financial forecasting. By shedding light on the potential of deep learning techniques to enhance predictive accuracy and robustness in cryptocurrency markets, our research offers valuable insights for stakeholders navigating this rapidly evolving landscape.</p> Dr. S. SAJITHABANU A. Asrin Mahmootha Samundeeswari K S. Sangeetha S. Rethinavelan Copyright (c) 2024 Journal of Computer Science Engineering and Software Testing 2024-05-20 2024-05-20 10 2