Cryptocurrency Price Prediction Using Parallel Gated Recurrent Units

2025-12-27

AI Summary

To address the low prediction accuracy and poor robustness caused by severe price volatility in cryptocurrencies such as Bitcoin, this paper proposes a Parallel Gated Recurrent Unit (PGRU) model. The PGRU introduces a novel multi-branch parallel RNN architecture, where each branch independently processes heterogeneous price features—e.g., open price, close price, and volatility—enabling feature decoupling to reduce data dependency and computational overhead, while effectively fusing multi-source temporal information. Experimental results demonstrate that the PGRU achieves MAPEs of 2.641% and 3.243% under prediction windows of 15 and 20 time steps, respectively, significantly outperforming mainstream RNN and Transformer-based baselines. Moreover, it requires fewer input features and incurs lower training and inference costs, thereby achieving a favorable trade-off among high accuracy, strong robustness, and lightweight design.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Natural Language Processing: Safety and Robustness
• Computer Vision: Multi-modal Vision

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Security and Privacy: Cryptocurrency and smart contracts

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.

Problem

• Predict cryptocurrency prices using parallel gated recurrent units
• Address price forecasting challenges in cryptocurrency investments
• Improve prediction accuracy with lower computational cost

Innovation

• Parallel Gated Recurrent Units for cryptocurrency price prediction
• Recurrent neural networks process distinct price features independently
• Combining parallel outputs with a neural network for forecasting