arXiv:2605.28531v1 Announce Type: new Abstract: Multi-step-ahead forecasts are often updated as new observations become available, since shorter forecast horizons typically improve forecast quality. However, such improvements come at the cost of forecast instability, i.e., variability in forecasts for the same target period. This instability can trigger costly changes to plans formulated based on the forecasts and may erode trust in the forecasting system. In this work, we integrate forecast stability alongside forecast quality into the training of distribution-free probabilistic time-series f
The proliferation of real-time data and demand for continuous, reliable AI-driven decision-making highlight the need for stable probabilistic forecasts.
Improving forecast stability directly addresses a critical weakness in AI systems used for planning and operations, reducing costly disruptions and building user trust.
This advancement proposes a method to integrate forecast stability into the training of distribution-free probabilistic time-series models, suggesting a path to more robust AI predictions.
- · AI forecasting system developers
- · Logistics and supply chain management
- · Financial services with real-time risk assessment
- · Autonomous systems
- · Systems heavily reliant on unstable, frequently updated forecasts
- · Organizations with low tolerance for forecast-induced plan adjustments
Increased operational efficiency and reduced planning friction in industries using AI forecasts.
Accelerated adoption of advanced AI forecasting across critical infrastructure and commercial sectors due to enhanced reliability.
Potentially enables new classes of autonomous decision-making systems where forecast stability is paramount, expanding AI's operational scope.
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Read at arXiv cs.LG