## 3 Framework of Forecasting AOG Models in Aviation
### Infrastructure for Real-Time Forecasting  
The successful deployment of real-time forecasting in aviation, particularly for predicting Aircraft On-Ground (AOG) incidents, depends on the availability of a robust, scalable, and secure digital infrastructure that can integrate both predictive analytics and operational workflows. At the foundation of this infrastructure lies the Aircraft Health Monitoring System (AHMS), such as Airbus's Skywise or Boeing's Airplane Health Management (AHM). These platforms are designed to collect high-frequency telemetry data from critical onboard systems, including engines, hydraulic circuits, landing gear assemblies, and avionics. The real-time nature of this data is essential for detecting early signs of component degradation or failure. To facilitate efficient transmission and processing, these sensor data are routed through Internet of Things (IoT) gateways, which serve as intermediary nodes capable of edge-level preprocessing and protocol normalization. These gateways interface with cloud ingestion services offered by providers such as Amazon Web Services (AWS IoT Core), Microsoft Azure IoT Hub, or Google Cloud Pub/Sub, which provide the necessary throughput, fault tolerance, and scaling capacity to support continuous data streams from geographically distributed aircraft fleets. Once ingested, the data must be organized within a high-availability data lake or stream processing architecture, typically built on platforms such as Apache Kafka or Google BigQuery. These structures enable the timely execution of forecasting algorithms and sup  
Figure 3: Success levels toward predictive maintenance for airline [20]. port long-term historical trend analysis. Forecasting models such as Holt-Winters or anomaly detection engines are then deployed as containerized microservices using Docker or orchestrated via Kubernetes clusters, allowing for modular updates, parallel computation, and system resilience. In environments where on-demand computation is preferred, serverless frameworks (e.g., AWS Lambda, Google Cloud Functions) provide the flexibility to execute specific predictive tasks without the overhead of persistent infrastructure. The visualization and operationalization of these predictive insights are handled through real-time dashboards and alerting systems. Tools such as Power BI, Grafana, or proprietary airline systems like eCentral8 and RAIDO are configured to display failure likelihood scores, threshold breaches, and recommended maintenance interventions. These interfaces are typically integrated with existing Maintenance, Repair, and Overhaul (MRO) planning systems to facilitate rapid decision-making by line engineers, inventory controllers, and maintenance schedulers. Equally important are the governance and compliance frameworks under which this infrastructure must operate. Given the sensitivity of flight operations data, the system must adhere to stringent cybersecurity protocols, including role-based access controls, data encryption (both in transit and at rest), and audit logging. Additionally, the system must remain compliant with international regulatory frameworks such as those defined by the European Union Aviation Safety Agency (EASA) and the Federal Aviation Administration (FAA), which mandate full traceability of maintenance decisions and restrict the use of non-transparent predictive systems in safety-critical environments. Looking forward, one of the most promising advancements lies in cross-airline data federation, whereby anonymized datasets from multiple carriers can be pooled to enrich model training and improve generalizability across different operational environments. Achieving this will require not only technical interoperability but also the establishment of industry-wide data governance standards and secure collaboration agreements. Such a transformation would mark a significant evolution from isolated predictive systems to a truly collaborative, data-driven ecosystem in commercial aviation.