HEPA combines self-supervised JEPA pretraining on time series representations with horizon-conditioned finetuning to predict rare events via survival CDFs, outperforming PatchTST, iTransformer, MAE, and Chronos-2 on at least 10 of 14 benchmarks while using an order of magnitude fewer tuned params.
JEPA imposes causal structure: the encoder sees only the past, the target encoder sees only the future
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HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series
HEPA combines self-supervised JEPA pretraining on time series representations with horizon-conditioned finetuning to predict rare events via survival CDFs, outperforming PatchTST, iTransformer, MAE, and Chronos-2 on at least 10 of 14 benchmarks while using an order of magnitude fewer tuned params.