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

DOI

10.2991/978-94-6463-408-2_45How to use a DOI?

Keywords

ZEC Price Forecast; Deep Learning Models; Neural Network

Abstract

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.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiakun Lian
PY  - 2024
DA  - 2024/05/07
TI  - Comparative Analysis of LSTM, GRU and Transformer Deep Learning Models for Cryptocurrency ZEC Price Prediction Performance
BT  - Proceedings of the 9th International Conference on Financial Innovation and Economic Development (ICFIED 2024)
PB  - Atlantis Press
SP  - 396
EP  - 405
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-408-2_45
DO  - 10.2991/978-94-6463-408-2_45
ID  - Lian2024
ER  -