ForeSPECT: A Model-Driven Framework for Validation and Traceability in Forecasting Systems
Bibliographic record
Abstract
Organizations increasingly rely on forecasting systems to anticipate future conditions and inform their strategic planning. However, current practices for specifications of these systems are scattered across workflows. Moreover, these specifications are either loosely defined or tied to data representation formats that lack domain awareness and offer only superficial validation. These limitations make it difficult to ensure correctness, enforce compliance, and trace qualitative adjustments across forecasting workflows. To address these challenges, we propose ForeSPECT, a model-driven framework for Forecasting with Semantic Provenance, Evaluation, Compliance, and Traceability. The framework introduces a metamodel that serves as the foundation for semantic validation and adjustments traceability, enabling early detection of domain-specific inconsistencies that conventional schema-based rules often miss. Our approach shows promise based on evaluation with nine unseen real-world datasets, achieving 77.7% mapping coverage between the metamodel and actual time-series record entities. It further demonstrates better performance in detecting errors earlier than pipeline-based methods, while ensuring 100% forward and 91% backward traceability of adjustments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".