The Create normalised values zone enables the standardization of the actual value of each category. The concepts handled here are explained in this data preparation [article](article:11). The details of each recipe are described below.

![Screenshot 2023-01-16 at 09.43.25.png](ECblPE0pJyBz)

The first [group by recipe](recipe:compute_historical_ts_data_normalised_by_date_1) followed by the [window recipe](recipe:compute_historical_ts_data_normalised_windows_1) are used to flag the first date of the test set in order to store this value as variable and use it as a condition in split recipes along the flow. 

The [filter recipe](recipe:compute_historical_ts_data_filtered) filters the [historical_ts_data](dataset:historical_ts_data) dataset in order to compute the minimum and maximum values of the target variable only on the train dataset in order to avoid bias.  

The [group by recipe](recipe:compute_train_values_by_subcategory) outputs the min and max values for each category which are added to the [historical_ts_data_joined](dataset:historical_ts_data_joined) through the [join recipe](recipe:compute_data_stacked_joined). 

The [prepare recipe](dataset:compute_data_stacked_joined_prepared) at the end of the zone calculates the normalised value of the target variable for each observation. 



