SPLICE couples JEPA-based latent diffusion with adaptive conformal inference to deliver accurate time-series inpainting with 93-95% empirical coverage on load datasets.
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NARFIMA integrates ARFIMA long memory with neural networks and exogenous variables to forecast BRIC exchange rates, establishes asymptotic stationarity, uses conformal prediction for uncertainty, and outperforms benchmarks empirically.
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SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting
SPLICE couples JEPA-based latent diffusion with adaptive conformal inference to deliver accurate time-series inpainting with 93-95% empirical coverage on load datasets.
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Neural ARFIMA model for forecasting BRIC exchange rates with long memory
NARFIMA integrates ARFIMA long memory with neural networks and exogenous variables to forecast BRIC exchange rates, establishes asymptotic stationarity, uses conformal prediction for uncertainty, and outperforms benchmarks empirically.