Keywords
Sustainable investment, Cryptocurrency analytics, Predictive Analysis mode, Market sustainability, financial decision-making, Blockchain Technology, Long Short-Term Memory
Article Type
Original Article
Abstract
The popularity of cryptocurrencies has skyrocketed in the last several years due to the introduction of blockchain technology (BCT). Herein, we are navigating the intersection of sustainable market investment and cryptocurrency predictive analysis against the backdrop of a dynamic and evolving financial landscape marked by the surge of digital assets. This study's goal is to construct the predictive analysis model (PAM) which incorporates Long Short-Term Memory (LSTM) capabilities to predict the price of Bitcoin with high accuracy the next day and to identify the variables that influence price. In constructed PAM, we are using a comprehensive methodology to study temporal correlations within minute-by-minute bitcoin data using preprocessing, sophisticated machine learning algorithms, and data exploration. Our findings demonstrate the effectiveness of the LSTM model in forecasting bitcoin behavior, offering detailed information that is essential for long-term market investing.
How to Cite
Mohamed, Mona and Gharib, Mona
(2024)
"PAM: Cultivate a Novel LSTM Predictive Analysis Model for the Behavior of Cryptocurrencies,"
Sustainable Machine Intelligence Journal: Vol. 6:
Iss.
1, Article 5.
DOI: https://doi.org/10.61356/SMIJ.2024.66101
Available at:
https://smij.sciencesforce.com/journal/vol6/iss1/5
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.