An average of the values in the last *n *time periods. As each new observation is added, the oldest one is dropped. A smoothed estimate of the level can be used to forecast future levels. Trends can be estimated by averaging changes in the most recent *n′* periods (*n′* and *n* generally differ). This trend can then be incorporated in the forecast. The value of *n *reflects responsiveness versus stability in the same way that the choice of smoothing constant does in exponential smoothing. For periods of less than a year, if the data are subject to seasonal variations, *n *should be large enough to contain full cycles of seasonal factors. Thus, for monthly data, one could use 12, 24, or 36 months, and so on. Differential weights can be applied, as is done by exponential smoothing.