Year: 2026
Type: preprint
Abstract: Long gaps in time series are difficult to reconstruct reliably, and in power systems imputed load values inform dispatch and planning. Existing generative imputers are accurate but provide no finite-sample reliability guarantees under changing conditions. We introduce SPLICE (Self-supervised Predictive Latent Inpainting with Conformal Envelopes), which couples a JEPA encoder, a conditional latent bridge, an hourly-conditioned decoder, and Adaptive Conformal Inference (ACI). Controlled ablations indicate that long-horizon fidelity is driven more by the predictive representation than by generative complexity: under a matched bridge and decoder, JEPA outperforms reconstructive VAE and contrastive TS2Vec encoders, and once the representation is fixed a 5-step flow sampler matches or improves on a 50-step DDIM sampler at a 5-10x speedup. The same ordering is recovered on twelve hydrological catchments. Across thirteen load datasets at 91-day gaps, SPLICE attains the lowest mean Load-only MSE (0.056), winning 9 of 12 non-degenerate datasets against seven established baselines, and the best mean CRPS (0.182, 7.8% below the strongest competitor), while ACI holds 93-95% empirical coverage where static calibration under-covers by up to 7.5 percentage points. A pooled JEPA encoder transfers to four held-out datasets with only bridge and decoder adaptation.
Source: arXiv (Cornell University)
Author: Zinflou, Arnaud
Institution: Hydro-Québec
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7160079269