Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty
arXiv:2606.04342v1 Announce Type: new Abstract: Multi-step time series forecasting (MSF) is commonly evaluated using point-wise error metrics such as mean squared error (MSE), implicitly treating the conditional mean as a sufficient target. We show that this can be misleading under conditional uncertainty, where the conditional expectation becomes unrepresentative of typical realized values at longer horizons. We formalize this effect through a conditional uncertainty gap and prove that whenever this gap is nonzero, no deterministic predictor can simultaneously minimize MSE and match the margi
This research highlights limitations in current AI forecasting methodologies, particularly as AI is increasingly deployed in complex, uncertain environments requiring more robust predictive models.
A strategic reader should care because reliance on flawed evaluation metrics in AI forecasting can lead to suboptimal decisions, especially in critical applications where conditional uncertainty is high.
This research suggests a necessary evolution in how multi-step time series forecasting models are evaluated and developed, moving beyond simple MSE-optimality to account for conditional uncertainty.
- · Researchers in probabilistic AI
- · AI model auditing firms
- · Developers of robust forecasting platforms
- · Sectors with high conditional uncertainty (e.g., climate modeling, finance)
- · Companies relying solely on MSE-optimized models for critical forecasting
- · AI developers who do not adapt their evaluation metrics
- · Legacy AI forecasting systems
AI developers will need to adopt more sophisticated evaluation metrics beyond MSE for time series forecasting, especially for longer horizons.
Increased demand for AI models that can quantify and manage conditional uncertainty will emerge, leading to new research and product development.
Improved forecasting accuracy in critical domains (e.g., supply chains, energy grids) could lead to more resilient systems and better resource allocation, but also highlights vulnerabilities of current systems.
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Read at arXiv cs.LG