Diffusion-based data augmentation for short-term multivariate energy prediction in data-scarce scenarios

This study explores diffusion-based generative modeling as a data augmentation strategy to improve forecasting in data-scarce scenarios. Using the ETTh1 multivariate energy dataset, we evaluate point and quantile forecasting across multiple forecasting architectures (XGBoost, LSTM, BiLSTM). Synthetic samples are generated via the Diffusion-TS framework for the neural models only, and incorporated at varying synthetic-to-real ratios. Results show that BiLSTM models benefit substantially in point