While forecasters are naturally tuned to pursue accuracy, that accuracy always comes with a cost. As we saw in the M4 results and elsewhere, the most accurate methods may be orders of magnitude higher in cost than marginally less accurate methods. In this article, Stephan Kolassa describes several methods for handling multiple levels of seasonality within a time series, and considers the tradeoff between forecast accuracy and the cost of producing the most accurate forecasts.