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.

I am currently working as a Data & Applied Scientist at NAV CANADA, where I design and implement advanced forecasting models for strategic planning and develop systems for predictive Air Traffic Management (ATM) performance analytics. My work focuses on leveraging Machine Learning, Predictive Modeling, and Data Science to optimize operational efficiency and support data-driven decision-making in aviation.

I hold a Ph.D. in Computer Science from McGill University, where I transitioned from a Master’s program in 2017 to a fast-tracked Ph.D. in 2019 under the mentorship of Professor Gunter Mussbacher. My doctoral research centered on building a recommendation system for requirements engineering, enabling practitioners to rapidly create domain models from informal natural language requirements. This system utilized Natural Language Processing (NLP) and Machine Learning to extract domain knowledge and construct queryable trace models as knowledge graphs, enhancing explainability and user interaction.

Prior to joining NAV CANADA, I gained extensive industry experience at National Research Council Canada, Bombardier Aerospace, and Accenture, where I applied my expertise in Data Science, Machine Learning, and Software Engineering to design predictive analytics systems and enterprise applications.