Using Deep Learning Models to Predict Cryptocurrency Prices - Kadhim Majeed , Zahraa; Abbas Abdullah, Haider; Ali Khaleel, Ameer (Iraqi Journal for Administrative Sciences, 2026)
The study aims to evaluate the effectiveness of deep learning models, specifically the Long and Short Term Memory (LSTM) model and the Gate Recurring Unit (GRU) model, in predicting the price of the cryptocurrency Bitcoin, based on a weekly time series over a period of ten years, from January 4, 2015 to December 29, 2024, with 522 observations.,the Long and Short Term Memory Network (LSTM) model structure and the Gate Recursive Unit (GRU) model were constructed using the R programming language and a set of libraries belonging to this language, the most important of which is the Keras library, which is well-known in the field of machine learning. The study adopted an analytical approach to compare two models and choose the optimal model based on performance metrics, namely The study reached several conclusions, the most important of which is the superiority of the LSTM model over the GRU model in predicting Bitcoin prices.In light of this, the study presented several recommendations, the most important of which is relying on the Long Memory Network (LSTM) model in forecasting in future studies.