arXiv:2606.16356v1 Announce Type: new Abstract: We study uncertainty quantification for aggregated forecasting tasks such as annual totals and year-over-year growth rates. We propose SA-MSCP, a simulation-augmented multi-step split conformal method that generates future paths from cross-validated residuals using a block bootstrap and constructs prediction intervals from empirical quantiles. Experiments show that SA-MSCP improves empirical coverage over a simulated-path baseline for aggregated and growth-rate targets. Our results demonstrate that simulation-enhanced conformal calibration is an
The increasing sophistication of AI models and the demand for reliable probabilistic forecasting necessitate advancements in uncertainty quantification.
Improved methods for quantifying uncertainty in aggregated forecasts are crucial for better decision-making in various strategic and financial domains.
Enterprises and governments will have access to more robust and accurate prediction intervals for critical aggregated metrics, reducing risks associated with forecasting.
- · Financial analysts
- · Supply chain planners
- · Econometric modelers
- · AI/ML consulting firms
- · Traditional statistical forecasting methods
- · Organizations relying on heuristic uncertainty estimates
More reliable aggregated forecasts lead to better financial planning and resource allocation.
Increased confidence in AI-driven forecasts could accelerate the adoption of autonomous decision-making systems.
Industries reliant on long-term aggregated projections might see reduced volatility due to more accurate future scenario planning.
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Read at arXiv cs.LG