TITLE:
Carbon Price Prediction for the European Carbon Market Using Generative Adversarial Networks
AUTHORS:
Yuzhi Chen
KEYWORDS:
Carbon Price Prediction, Generative Adversarial Network, Long Short-Term Memory Network, Convolutional Neural Network, Wasser-Stein Distance
JOURNAL NAME:
Modern Economy,
Vol.15 No.3,
March
18,
2024
ABSTRACT: Carbon price prediction is an important research interest. Deep learning
has latterly realized triumph because of its mighty data processing competence.
In this paper, a carbon price forecasting model of generative antagonistic
network (GAN) with long short-term memory network (LSTM) as the generator and
one-dimensional convolutional neural network (Conv1d) as the discriminator is
proposed. The generator inputs historical carbon price data and generates
future carbon prices, while the discriminator is designed to differentiate
between the real carbon price and the generated carbon price. For verifying the
validity of the proposed model, the daily trading price of the European carbon
market is selected for numerical simulation, and compared with other prediction
models, the GAN proposed has good property in carbon price prediction.