Forecasting Bitcoin Value with Hybrid LSTM-GRU Neural Networks

Abstract

In the volatile cryptocurrency market, accurately forecasting Bitcoin prices is crucial yet challenging, carrying significant economic implications. This paper presents a novel hybrid model that merges the predictive capabilities of Long Short-Term Memory (LSTM) networks with the computational efficiency of Gated Recurrent Units (GRU). This integration is designed to simultaneously capture long-term dependencies and short-term fluctuations inherent in Bitcoin price dynamics, thus providing a comprehensive analysis framework. The model utilizes a meticulous architecture starting with an input layer that normalizes data to address price variability, followed by LSTM layers that interpret long-term trends, and GRU layers that refine insights based on short-term variations. Evaluated using a dataset divided into training, validation, and testing phases and optimized with the Adam algorithm, the model’s performance surpasses traditional forecasting methods and standalone neural networks. Metrics such as RMSE, MAE, and R2confirm its superior predictive accuracy, with significant improvements over benchmarks like ARIMA, standalone LSTM, and GRU models. This breakthrough highlights the Hybrid LSTM-GRU model’s potential as a transformative tool for investors and analysts navigating the complexities of the cryptocurrency market.