The Results preparation zone builds the two datasets, [historical_ts_data_test_filtered](dataset:historical_ts_data_test_filtered) and [results_data_joined_stacked](dataset:results_data_joined_stacked)), that will be used to calculate metrics and create other tables to use in the [dashboard](dashboard:j4Pjdob).

![Screenshot 2023-01-13 at 17.45.30.png](5BY9Xyv0dbWb)

The [group by](recipe:compute_historical_ts_data_test_scored_group) recipe, followed by the [window](recipe:compute_historical_ts_data_test_scored_windows), the [prepare](recipe:compute_historical_ts_data_test_scored_prepared) and the [join](recipe:compute_historical_ts_data_test_filtered)  recipe enable to select, for each datapoint of the dataset including the forecast results of the test set, the correct corresponding horizon. The objective of this preparation step is to create a [dataset](dataset:historical_ts_data_test_filtered) that will be used to compute the performance metrics showcased in the [dashboard](dashboard:j4Pjdob). 

The [filter](recipe:compute_evaluation_results_H1) recipe filters the evaluation results over the train set to keep only the prediction made over the first horizon. The objective here is to keep only one predicted value per date in order to be able to build the graphs.

The [stack](recipe:compute_forecasts_dataset_final) recipe combines the rows of the previously filtered dataset and the forecasted values over the next horizons. 

The [join](recipe:compute_results_data_joined) recipe is simply used to create columns having values only for the forecasted horizon to allow the design of some graphs in the [dashboard](dashboard:j4Pjdob). 

The final [stack](recipe:compute_results_data_joined_stacked) recipe adds the rows of the observations that were not included in the advanced forecast because they were used as the minimum observation needed to create the simple forecast. 