# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 3 Employee Absenteeism Data
### Data Attributes and the Structure of the Data Instances  
In Table 1, we are presenting general structure of data instances used in the learning tasks. The datasets to predict employee absences are organized into _descriptive attributes_ and _target attributes_. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **(a)** \\ \hline
**Descriptive Attributes** & \multicolumn{2}{c}{**Target Attribute**} \\ \hline Demographic profile & Absence profile (up to week K) & Absence in \(W_{K}\) (number of days) \\ \hline  & **(b)** \\ \hline
**Descriptive Attributes** & \multicolumn{2}{c}{**Target Attributes**} \\ \hline Demographic profile & Absence profile (up to week K) & Absence in \(W_{k}\) & Absence in \(W_{K+1}\) & Absence in \(W_{K+2}\) & Absence in \(W_{K+3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The structure of the data instances used for the learning task. (**a**) Single-target regression; (**b**) Multi-target regression (example for 4 week ahead prediction). Figure 1: The distribution of different company types. Figure 2: (**a**) Regional distribution (**b**) Statistical regions in Slovenia. **Descriptive attributes** include all variables that provide nominal information about individual employees. These are categorised into two main types: _Demographic profile_ and _Absence profile_. The **demographic profile**, detailed in Table 2, contains several attributes that describe the demographic characteristics of the employees. These include the company type, with 33 predefined categories that specify the industry and company structure; the job type, which categorizes an employee's role into one of six different categories based on their responsibilities and department; the statistical region in Slovenia where the company is registered, which is selected from 12 different regions and provides a geographical context; the working hours specified in the employee's employment contract, which helps differentiate between part-time, full-time and other working hours; the type of employment, which indicates whether it is part-time, full-time, or student employment, all of which can have a potential impact on absenteeism behavior; and the number of years the employee has been with the company, which can affect their stability and likelihood of absenteeism. These attributes are important for analysing factors that may influence employee attendance and are crucial for any predictive modelling related to workplace absenteeism. The **absence profile** is composed of lagged attributes. Lagged attributes in predictive modelling are historical data points that are used as input for the prediction of future events. In the context of predicting employee absences, lagged attributes are particularly useful for capturing patterns and trends over time that indicate an employee's future absence behaviour. Our dataset contains two types of lagged attributes: statistical and seasonal. **Statistical lagged attributes** are derived by aggregating past data over specific time periods, labelled XY in Table 3 (from 1 week to 12 months), to capture both short-term and long-term trends that help predict future outcomes. These attributes, detailed in Table 3, provide a comprehensive overview of an employee's absence history in three different forms. Firstly, we take into account the total number of days an employee was on vacation or sick leave during a given period to obtain an aggregate count that shows the general patterns of absence. In addition, we separate these counts into short-term (three days or less) and long-term (more than three days) categories for both vacation and sick leaves. This distinction not only provides insight into the duration of past absences, but also helps to predict similar trends in the future. Finally, the dataset also contains daily absence figures, labelled Z, which indicate the exact days of the week on which an absence occurred, e.g., Mondays or Fridays.