## Flow zone: Demand forecasting modeling
![demand_forecasting_zone.png](zBIQP8aHis6M)
This zone contains all the steps involved in preparing the training data, training and deploying the model, generating forecasts and backtest.

### Training Data Preparation

A first recipe is used to filter the input data and create the training dataset by excluding the most recent period covered by the forecast horizon, based on the configuration.

This ensures that the model is trained only on historical data and not on future periods to be forecasted.

### Model Training and Deployment

A **Train recipe** is then used to train the demand forecasting model. The model is automatically deployed upon completion and made available for scoring in the next steps.

### Evaluation and Forecasting

Once the model is deployed:
- An **Evaluate recipe** is used to **backtest the model** on historical data. This step reuses external features if available (e.g., pricing).
- A **Scoring recipe** is used to generate the **forecast for the future periods** based on the trained model.

If you're looking for more details about how the model is trained, evaluated, and how forecasts are generated, please refer to the dedicated article: [Generating Forecasts](article:3)