When will your model fail? TreeAlert: a method for predicting extreme forecasting failures

The heavy reliance on time-series forecasting in critical decisions gives rise to an urgent need to address extreme forecasting failures that significantly impact organizations. Such errors can arise due to the nature of forecasting algorithms and their interaction with data patterns. This study distinguishes itself by foregrounding a critical-yet-previously-overlooked question: when are models likely to fail? This paper makes several contributions: it introduces a generalizable framework for predicting pattern-based model failures; it presents TreeAlert—a model-agnostic, interpretable, and operationally integrable method; it proposes the lift metric for model tuning and evaluation; and it expands the field of error analysis by shifting focus from error magnitude to predicting the timing of likely extreme failures. Guided by principles of pattern-based error prediction, we develop TreeAlert, a novel method designed to identify patterns of extreme forecasting errors and predict the timing of future extreme failures. TreeAlert uses regression trees to detect failure-prone conditions, linking them to contextual and timing patterns. We demonstrate TreeAlert using electricity demand and heart rate data, applying it to point forecasts and forecast distributions. By forecasting the timing of future failures, TreeAlert transforms retrospective error analysis into a forward-looking tool that empowers decision-makers to proactively anticipate vulnerabilities and enables data scientists to improve algorithmic weaknesses.