The input dataset preparation zone enables to create the final version of the dataset that will be used to train and test the regression model ([historical_ts_data_forecasted](dataset:historical_ts_data_forecasted)) along with the dataset containing the values to forecast ([to_forecast_data_forecasted](dataset:to_forecast_data_forecasted)). 

![Screenshot 2023-01-13 at 14.56.00.png](QzTIeVjdpxbl)

The first [join recipe](recipe:compute_complete_dataset) combines the columns of four datasets: 
- [forecasted_ts_features_joined_lag](dataset:forecasted_ts_features_joined_lag) which contains the date, the category, the horizon, the time series forecast, and the lag values columns.
-  [historical_ts_data_normalised](dataset:historical_ts_data_normalised) that includes the manual forecast, the actual value, and the minimum and maximum values used to normalise the data. 
-  [naive_forecast_data](dataset:naive_forecast_data) including the non-normalised actual value and the naive forecast. 
- [historical_ts_data](dataset:historical_ts_data) is allowing to recover selected drivers' values. 

The second [join recipe](recipe:compute_to_forecast_data_forecasted) combines the columns of three datasets, two of them already used in the previously detailed join recipe: 
-  [to_forecast_data_flag_horizons](dataset:to_forecast_data_flag_horizons) that includes the date, the category, the horizon, the manual forecast and the selected drivers' values. 
- [forecasted_ts_features_joined_lag](dataset:forecasted_ts_features_joined_lag) which contains the time series forecast and the lag values columns.
-  [naive_forecast_data](dataset:naive_forecast_data) including the naive forecast. 


