Contrastive Forecasting for Time Series

This talk introduces Contrastive Forecasting, an unsupervised method for time series prediction that learns entirely in latent space. The approach is grounded in contrastive divergence and uses a joint-embedding predictive architecture (JEPA) to align predicted future states with actual outcomes while distinguishing them from carefully selected negative samples.