Bitcoin Price Forecasting with Generative Adversarial Networks

Abstract

Forecasting bitcoin prices using deep learning techniques has gained significant attention recently. Despite the success of Generative Adversarial Networks (GANs) across various domains, there is a crucial issue in bitcoin price prediction: the ability to accurately capture correlations between temporal data points. Moreover, GANs face notable inherent challenges, particularly training instability and mode collapse. In this paper, we employ windowing and conditioning techniques to capture correlations between prices and other relevant features such as technical indicators. Additionally, we use a hybrid Long Short-Term Memory (LSTM)-based generator that combines a convolution layer and LSTM to improve learning from temporal data. Appropriate loss functions are also utilized for the discriminator and generator to enhance training stability and mitigate mode collapse. The proposed method was evaluated on the historical Bitcoin data sourced from the Yahoo Finance website. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art methods. The source code for the proposed method is available on GitHub: https://github.com/mahdimanian/draganbtc/