Innovative Pattern Extraction and Synthetic High-Frequency Data Generation in European Carbon Emmision Markets Using GAN Networks

This study analyzed high-frequency tick-by-tick data of carbon emission allowances in the European emissions trading system, resampled into 5-minute intervals, to identify innovative price patterns and extract key features for anticipatory insights. A dataset of pattern sequences and features was constructed, and a GAN model with LSTM and Self-Attention layers was used to generate synthetic sequences, producing artificial interval data. The synthetic data was validated using key financial metrics, demonstrating it effectively emulates real market behavior. The results support applications in risk management, algorithmic trading, market simulation, and stress testing under varying conditions.