Economic Forecasting Model

Section 4: Machine Learning Model Design & Implementation

4.1 Problem Formulation

Economic forecasting is modeled as a supervised time-series regression problem.

  • Input: Multivariate time-series sequence.
  • Output: Forecasted value(s) over a defined horizon.

    • 4.2 Model Selection

  • Baseline: ARIMA / SARIMA, Linear Regression.
  • Production: LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), Transformers.

    • 4.3 Architecture: LSTM

    LSTM is preferred for macroeconomic data due to its stability on lower-frequency series.

  • Input layer (time window × features).
  • 1–3 stacked LSTM layers.
  • Dropout for regularization.
  • Dense output layer.

    • 4.4 Training Strategy

  • Chronological train/validation/test split.
  • No shuffling of time-series data.
  • Loss: MSE (Mean Squared Error) or MAE (Mean Absolute Error).
  • Optimizer: Adam.
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