*3.1. ATFCM Regulations Database*

Firstly, the format of the output is explained, as the entire regulatory prediction model will depend on it. As the aim of this paper is to predict ATFCM regulations based on a strong temporal component, it is fundamental how these regulations are determined.

The first step is to filter the regulations to be predicted. In this paper, regulations based on the lack of ATC capacity will be predicted. Based on the causes proposed by [10], the following causes shall be considered:


These causes are related to the lack of capacity of the ATC system or of certain airports. In addition, it has been decided to organise the ATFCM regulations in 10-min periods and in days of the year. In this way, all the time that the airspace was regulated will be arranged in a matrix as in Figure 2. The rows of the matrix will be the day of the year under analysis, and the columns will be the 10-min period, with the start of the period marked as the column name.

In this matrix, it will be noted in which periods and days when the sector will be regulated (1) and when it will not be regulated (0). With this format, a simple picture of when the sector will or will not be regulated emerges. Once the format of the output has been determined, the input variables, both the time and traffic databases, need to be adapted.

Furthermore, by arranging the data in this way, it can be established that the bestfitting machine learning model is a binary classification model. Classification problems are already widespread in the industry [26,27], and binary classification is the most widespread type of model. In a binary classification model, the training and test data are distributed into two labels, 0 (in this case, the sector is not regulated) and 1 (in this case, the sector is regulated). Therefore, by training the model with only two labels, the model will predict only the two labels. This type of model has the advantage of facilitating training by having fewer classes, and of being a model with simpler explainability than a model with a larger number of classes to classify.

**Figure 2.** ATFCM Regulations array.

This will facilitate the development and implementation of this model. In addition, a Random Forest model has been chosen to carry out this classification as it is an algorithm that works well in problems of a different nature and that allows a correct analysis of the explainability of the model [28].
