ForeACT: A Model-Driven Workbench for Actionability Assessment of Forecast Changes

Abstract

Operational forecasts are frequently updated as new data, assumptions, scenarios, and model configurations become available. In decision-facing forecasting workflows, the practical question is not only whether a forecast has changed, but whether the change is stable, supported by sufficient confidence, and actionable. This paper presents ForeACT, a model-driven workbench for assessing the actionability of forecast changes. ForeACT introduces a forecast-actionability metamodel that represents key concepts, including forecast values, scenario assumptions, analytical methods, signals, and decision policies. The workbench transforms dataset-specific semantic mappings, methodology choices, and business context into a compiled forecast assurance model. Structural conformance is checked against constraints declared in the Ecore metamodel, while explicit analysis-readiness rules evaluate cross-element conditions required for forecast comparison and analysis. Analytical transformations then produce forecast revisions, revision magnitudes, volatility and confidence signals, and corresponding decision artifacts. These artifacts remain traceable to the selected forecast vintages, target periods, methods, thresholds, assumptions, and supporting evidence. We demonstrate ForeACT using a publicly available real-world electric vehicle sales dataset, from which we construct multiple forecast vintages to assess whether observed forecast changes require planning action or continued monitoring under uncertainty. The demonstration shows how model-driven representations and transformations make forecast-change interpretation explicit, configurable, and traceable for decision support.