Generative Pre-Trained Diffusion Paradigm for Zero-Shot Time Series Forecasting

This research explores the application of generative pre-trained diffusion paradigms, drawing parallels with successful large language models and vision models, for zero-shot time series forecasting. It highlights the potential of such generative paradigms to serve as powerful foundation models for complex time series data analysis.