## 4 Database, Available Features, and Target to Be Forecasted  
In order to study the relationship between different time series with data regarding GBV and other variables, we have accessed the database of Spanish INE (www.ine.es), where it is possible to freely find data from several decades related to demography, economy, employment market, education, energy, and so on. The data series are usually grouped by population groups (always disaggregated by sex), and also by territorial units. The frequency of data reporting can be monthly, quarterly, or annually. For our purposes, we have assembled the data by provinces and also the country's total. Spain has 50 provinces and 2 autonomous cities. We decided to select some examples as study cases then compare them with the evolution of the total country. With regard to the timescales, we decided to divide the data monthly as, on the one hand, this is the usual way of presenting the data in our database and, on the other hand, it offers sufficient granulometry to show the variable evolution. In Table 1, a brief description of the variables and the units utilized can be observed. All variables have been referred to population units (per capita) in order to make a fair comparison between territories. Although the Spanish Government has been collecting GBV casualties' data since 2003, we begin our database in 2009 in order to obtain a complete overview by avoiding any gaps in the early data of some variables. We also seek to reflect the changes introduced by the Organic Law on Measures Regarding Comprehensive Protection against Gender-Based Violence (LO 1/2004, December 28, 2004), some of whose measures were not fully implemented until some years later. Likewise, the most recent data may have a delay in their incorporation and be subject to revision, so full series data up to March 2020 have been used. With this approach, we have studied more than a decade of data. The chosen features (among the available ones) are related to:  
* Territorial: We study the time-series data for the entire country but also some provinces as examples, in order to test the validation of our purpose. * Date and season: We will explore the evolution of GBV within years, month by month. We will also include the quarter to evaluate the influence of the season, as indicated by previous works [59]. * Demography and population: Considering population can offer insights into the influence of big population areas, but some changes in demography can also provide explanations of the course of couples [60]. In this manner, marriages, separations, and births are included, but also the proportion of men vs. women. * Specific variables related to GBV: In this sense, there are some interesting variables available, such as:
* Calls to the special number 016. This is a phone number dedicated to providing information to survivors, but also to manage assistance (imperative or not). * Complaints: In particular, we will study the number of complaints presented to a court as the independent variable to be modeled and forecasted. Ultimately, we feel that complaints express the incidence of worst cases. * Security devices for tracking offenders: This kind of device is proposed by a judge in high-risk cases. * Protection orders: Also ordered by a judge in cases of high risk. Level of risk of aggression for the survivor: After a police evaluation, the cases are classified as unappreciated, low, medium, high, and extremely high. * Fatalities: Murdered victims of GBV. * Wealth and employment: The level of wealth in a region can be related to the levels of crime and violence. Similarly, levels of unemployment (male and female) can give an idea of the level of economic stability [61]. We differentiate between the inactive population (retired, disabled) and also the employed and unemployed population. * Education level: The relationship of illiteracy (male and female) and other educational levels (primary, secondary, university) with violence will also be studied, as previous literature indicates this point [62].