Economic Forecasting Model

Section 2: System Overview & Architecture

2.1 System Overview

The Economic Forecasting Model is designed as a modular, cloud-native machine learning system that automates the full lifecycle of economic prediction—from raw data ingestion to real-time forecast delivery and visualization.

2.2 functional Workflow

Data Acquisition: Automated retrieval from multiple sources.

Data Processing: Cleaning, normalization, and feature engineering.

Model Training: Deep learning models trained on historical data.

Model Validation: Statistical evaluation and backtesting.

Model Registration: Versioning and lifecycle management.

Inference & Forecasting: Real-time prediction via API.

Visualization & Reporting: Interactive dashboards.

2.3 Architectural Style

The system adopts a microservices-oriented architecture, combined with MLOps best practices.

Event-driven pipelines for data ingestion.

Containerized services for portability.

Stateless inference services for horizontal scaling.

2.4 Core System Components

Data Ingestion Service: Collects raw data and validates schemas.

Data Processing Layer: Handles missing values, outliers, and windowing.

Model Training Engine: Hyperparameter tuning and metric tracking.

Model Registry: Manages lifecycle, promotion, and rollbacks.

Inference API: Exposes model predictions to external systems.

Visualization Layer: Translates outputs into human-readable insights.