The article presents a new method for adjusting daily time series data to account for seasonal patterns and holidays. By combining an iterative STL-based seasonal adjustment routine with a RegARIMA model, the researchers developed a procedure that estimates and corrects for systematic effects in daily observations. They tested this method using currency circulation data from Germany and simulated time series, finding that it produces similar results to established monthly data adjustment methods. This new procedure fills a gap in current statistical practices by making it easier to adjust daily time series for seasonal and calendar effects.