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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. Yet evidence on their benefits remains mixed, and most st
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