Volume 23, Issue 3 (9-2026)                   jor 2026, 23(3): 0-0 | Back to browse issues page

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Mombeini H, Abolfathi E, Anvary Rostamy A A. Developing a Group Learning Prediction Model Using Deep Neural Networks for Bitcoin Cryptocurrency. jor 2026; 23 (3)
URL: http://jamlu.lahijan.iau.ir/article-1-2300-en.html
Developing a Group Learning Prediction Model Using Deep Neural Networks for Bitcoin Cryptocurrency
H. Mombeini , E. Abolfathi , A. A. Anvary Rostamy *
Department of Planning and Management, Tarbiat Modares University, Tehran, Iran , anvary@modares.ac.ir
Abstract:   (60 Views)
Bitcoin, as a financial instrument for trading and storing value, has gained widespread attention across various countries around the world. Recognized as the “gold” of cryptocurrencies, Bitcoin influences the price movements of other digital assets; therefore, forecasting its performance is of considerable importance. Understanding how to construct models that are capable of delivering reliable predictive outcomes has consequently become a central research focus. In this study, we develop five predictive models across nine time periods using time-series data. The first four models consist of deep neural network architectures, including LSTM, GRU, TCN, and BiLSTM, while the fifth model is a proposed ensemble-learning model. After constructing these models and analyzing their outputs, aggregated predictive models are generated using the outputs of all individual models along with the actual observed data. The findings of this research indicate that all models yield satisfactory and acceptable performance across the nine study periods; however, the ensemble learning models consistently outperform each individual model in all nine periods.

 
Keywords: Deep Neural Networks, Ensemble Learning, Bitcoin.
     
Type of Study: Research | Subject: Special
Received: 2026/03/16 | Accepted: 2026/07/18 | Published: 2026/09/11
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