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.