Economic Forecasting Model

Section 3: Data Pipeline Architecture & Implementation

3.1 Importance of the Data Pipeline

In economic forecasting, data quality and consistency are more important than model complexity. The pipeline is responsible for ensuring data is accurate, timely, and complete.

3.2 Data Sources

  • Macroeconomic Indicators: Inflation, GDP, Interest rates.
  • Financial Market Data: Exchange rates, Stock indices, Commodity prices.
  • Supplementary Data: Trade balances, Policy rates, Sentiment indicators.

    • 3.3 Data Storage Design

    The system uses a layered storage strategy:

  • Raw Data Layer: Immutable, source-specific, and time-stamped.
  • Processed Data Layer: Cleaned and ready for feature engineering.
  • Feature Store: Model-ready features, versioned and time-indexed.

    • 3.4 Feature Engineering

  • Temporal Features: Lags, rolling averages, volatility, growth rates.
  • Cross-Indicator Features: Correlation-based combinations, ratio metrics.
  • Scaling: Standardization fitted only on training data to prevent leakage.

    • 3.5 Pipeline Symmetry

    > The same transformations used during training must be applied during inference.

    To enforce this, feature definitions and transformation parameters are versioned and stored for use by the inference API.

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