Cryptocurrency Price Prediction Using Parallel Gated Recurrent Units

• URL: http://arxiv.org/abs/2512.22599v1
• Date: Sat, 27 Dec 2025 14:04:21 GMT
• Title: Cryptocurrency Price Prediction Using Parallel Gated Recurrent Units
• Authors: Milad Asadpour, Alireza Rezaee, Farshid Hajati,
• Abstract summary: This paper presents a new deep model, called emphParallel Gated Recurrent Units (PGRU) for cryptocurrency price prediction.<n>The proposed model achieves mean absolute percentage errors (MAPE) of 3.243% and 2.641% for window lengths 20 and 15.
• Score: 1.3884247760916029
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: According to the advent of cryptocurrencies and Bitcoin, many investments and businesses are now conducted online through cryptocurrencies. Among them, Bitcoin uses blockchain technology to make transactions secure, transparent, traceable, and immutable. It also exhibits significant price fluctuations and performance, which has attracted substantial attention, especially in financial sectors. Consequently, a wide range of investors and individuals have turned to investing in the cryptocurrency market. One of the most important challenges in economics is price forecasting for future trades. Cryptocurrencies are no exception, and investors are looking for methods to predict prices; various theories and methods have been proposed in this field. This paper presents a new deep model, called \emph{Parallel Gated Recurrent Units} (PGRU), for cryptocurrency price prediction. In this model, recurrent neural networks forecast prices in a parallel and independent way. The parallel networks utilize different inputs, each representing distinct price-related features. Finally, the outputs of the parallel networks are combined by a neural network to forecast the future price of cryptocurrencies. The experimental results indicate that the proposed model achieves mean absolute percentage errors (MAPE) of 3.243% and 2.641% for window lengths 20 and 15, respectively. Our method therefore attains higher accuracy and efficiency with fewer input data and lower computational cost compared to existing methods.

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