TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation

2026-06-09 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Environmental Science & Earth Systems · Depth: Expert, extended

Summary

TriHead-GAN, a Transformer-based adversarial framework, addresses the scarcity of city-level high-frequency carbon emission monitoring data by generating synthetic time series. Its core innovation is a triple-head discriminator that jointly supervises distributional authenticity via a Wasserstein critic, cross-variable dependency through leakage-free regression, and step-wise temporal smoothness via adjacent-difference prediction. The generator combines global self-attention with local temporal convolution, per-step noise injection, and an anti-smoothing loss. Evaluated on the Changsha Carbon, China Carbon, US Carbon, and ETTh1 datasets, TriHead-GAN achieves superior performance over six mainstream baselines in 18 of 20 settings, particularly on joint-distribution metrics like MMD, FID, and DS, and significantly improves downstream forecasting accuracy in low-resource scenarios. It also offers inference speeds three orders of magnitude faster than diffusion-based models.

Key takeaway

For Machine Learning Engineers developing generative models for multivariate time series in data-scarce domains like urban carbon monitoring, you should consider TriHead-GAN. Its triple-head discriminator and anti-smoothing loss significantly improve synthetic data fidelity and downstream forecasting utility, outperforming diffusion models in joint distribution metrics and offering much faster inference. This approach provides a robust solution for augmenting limited real data, enhancing predictive model performance, and reducing deployment costs.

Key insights

TriHead-GAN uses a triple-head discriminator and anti-smoothing loss to generate high-fidelity multivariate time series, especially for carbon emissions.

Principles

• Multivariate time series generation requires explicit supervision for cross-variable and temporal dynamics.
• Joint distribution fidelity benefits from multi-aspect adversarial supervision.
• Anti-smoothing losses prevent local variation collapse in generated sequences.

Method

TriHead-GAN's generator combines Transformer encoding, local temporal convolution, and per-step noise. Its triple-head discriminator uses parallel CNN branches for Wasserstein authenticity, leakage-free cross-variable regression, and causal adjacent-difference prediction, augmented by an anti-smoothing loss.

In practice

• Augment limited real data with TriHead-GAN synthetic windows.
• Use TriHead-GAN for city-level carbon emission monitoring.
• Apply to multivariate time series needing cross-variable consistency.

Topics

• Generative Adversarial Networks
• Time Series Generation
• Carbon Emission Monitoring
• Triple-Head Discriminator
• Transformer Architectures
• Data Scarcity Solutions

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.