In this paper, we focus on predicting an em-ployee's absence based on historical timesheet data. More specifically, based on one-year historical data, we want to examine how the size of the time window of the historical timesheet profiles influences the prediction power in the case of one-week ahead absenteeism prediction. In our case, the time window denotes an absence profile for a sequence of weeks that precede the target week, which is then used as a descriptor when building the predictive model. The data are obtained from MojeUre, a system for tracking and recording working hours and includes timesheet profiles of employees from different companies in Slovenia. We design different analysis scenarios and use a selection of regression algorithms from the Weka [1] data mining software as the primary tool for building predictive models. To analyse the influence of the window size on the predictive power, we use as indicators different performance evaluation measures. In general, we conclude that using an extended window size helps to achieve better predictive performance.