PAM: Cultivate a Novel LSTM Predictive Analysis Model for the Behavior of Cryptocurrencies
Mona Mohamed, Mona Gharib · Sustainable Machine Intelligence Journal · 2024
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.