Cryptocurrency Price Prediction Using Parallel Gated Recurrent Units

TL;DR

This paper introduces a novel deep learning model called Parallel Gated Recurrent Units (PGRU) that improves cryptocurrency price prediction accuracy by using parallel neural networks to process different features simultaneously, reducing computational costs.

Contribution

The paper proposes the PGRU model, a new deep learning approach that forecasts cryptocurrency prices more accurately and efficiently than existing methods by leveraging parallel recurrent networks.

Findings

Achieves MAPE of 3.243% and 2.641% for window lengths 20 and 15.

Outperforms existing methods in accuracy and computational efficiency.

Uses fewer input features to maintain high prediction performance.

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.