Cryptocurrency price forecasting - A comparative analysis of ensemble learning and deep learning methods

Abstract

Cryptocurrency price forecasting is attracting considerable interest due to its crucial decision support role in investment strategies. Large fluctuations in non-stationary cryptocurrency prices motivate the urgent need for accurate forecasting models. The lack of seasonal effects and the need to meet a number of unrealistic re-quirements make it difficult to make accurate forecasts using traditional statistical methods, leaving machine learning, particularly ensemble and deep learning, as the best technology in the area of cryptocurrency price forecasting. This is the first work to provide a comprehensive comparative analysis of ensemble learning and deep learning forecasting models, examining their relative performance on various cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin) and exploring their potential trading applications. The results of this study reveal that gated recurrent unit, simple recurrent neural network, and LightGBM methods outperform other machine learning methods, as well as the naive buy-and-hold and random walk strategies. This can effectively guide investors in the cryptocurrency markets.