Carbon Price Prediction for the European Carbon Market Using Generative Adversarial Networks

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.