Comparative Analysis of LSTM, GRU and Transformer Deep Learning Models for Cryptocurrency ZEC Price Prediction Performance

This paper delves into the intriguing realm of cryptocurrency price prediction, with a specific focus on Zcash (ZEC), employing a cutting-edge deep learning approach. The study introduces two crucial features, “close_off_high” and “volatility”, then systematically analyzes the correlations between these variables and the price of ZEC. By investigating the predictive accuracy of three prominent neural network architectures-Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the Transformer model-the study discerns that LSTM and GRU models outperform the others in forecasting ZEC’s price movements. Furthermore, the paper scrutinizes the influence of different activation functions on model performance, shedding light on the effectiveness of the linear activation function in this context. The research also addresses common challenges in predictive modeling, such as overfitting and multicollinearity. Moreover, it candidly acknowledges the limitations associated with solely focusing on a single cryptocurrency, recognizing that broader research efforts and interdisciplinary collaboration are required for a more comprehensive understanding of the ever-evolving cryptocurrency landscape. As the cryptocurrency market continues to evolve rapidly, this study provides invaluable insights for investors, offering a rational perspective on cryptocurrency investment. It underscores the importance of utilizing appropriate models and embracing interdisciplinary cooperation to navigate the complex and dynamic world of cryptocurrency. By bridging the gap between the cutting-edge world of deep learning and the financial market, this research paves the way for enhanced future investigations and more informed investment decisions.