Does Joint-Embedding Predictive Architecture Pretraining Help Time Series Forecasting?

Joint-embedding predictive architectures (JEPA) have emerged as a promising self-supervised pretraining paradigm for time series, learning representations by predicting target embeddings in latent space rather than reconstructing raw signals.