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

arXiv:2609.31680 · cs.LG, cs.AI, stat.ML · Submitted 2026-09-16 · Read on arXiv

cs.LG, cs.AI, stat.ML

Submitted: 2026-09-16

Updated: 2026-09-16

Terminology

• Joint-Embedding Predictive Architecture — 8× in this paper
• Benchmark — 7× in this paper · explained in 2 episodes
• Self-Supervised Learning — 5× in this paper · explained in 8 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Time Series Forecasting — 4× in this paper · explained in 3 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Decode — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• HiT-JEPA: A Hierarchical Self-supervised Trajectory Embedding Framework for Similarity Computation
• Joint Embeddings Go Temporal
• Is the reconstruction loss culprit? An attempt to outperform JEPA
• LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning
• SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Prediction
• LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting
• Transformers in Time Series: A Survey
• LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

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