Ensemble Model Based on Deep Learning for Forecasting Crypto Asset Futures in Markets

Abstract

This paper investigates an ensemble convolutional and recurrent neural network architecture for cryptocurrency price forecasting. The inherent volatility and noise in cryptocurrency time series pose considerable modeling challenges. The proposed ensemble model integrates convolutional neural networks (CNN) and gated recurrent units (GRU) to jointly discern spatial patterns and temporal dynamics. The model is trained on an extensive dataset comprising daily historical prices for major cryptocurrencies spanning January 2015 to October 2023. The time series data is structured into rolling input sequences of historical prices and target outputs as future price values. Comprehensive hyperparameter tuning is conducted to optimize model performance. Rigorous validation on held-out test data enables analysis of multi-step prediction accuracy. Results demonstrate that the ensemble CNN-GRU model achieves high forecasting proficiency. Evaluation metrics including Root Mean Squared Error quantify the model's efficacy in learning the nuanced volatility signatures of cryptocurrencies. Additionally, the high R-squared scores attained, including 0.99 for Bitcoin and Ethereum and 0.98 for Ripple, underscore the model's exceptional capacity to explain cryptocurrency price fluctuations. This substantiates the model's utility for generating actionable insights for investors and analysts in the cryptocurrency domain.