The article presents a new method to adjust for seasonal and calendar effects in daily time series data. By combining an iterative STL-based seasonal adjustment routine with a RegARIMA model, the researchers developed a procedure to estimate and correct for systematic effects and moving holidays in daily observations. They tested this method using currency circulation data from Germany and simulated time series, finding that it produces similar seasonally adjusted results as 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 variations.