| Abstract: | The objective of this research is to find the best methods of automatically monitoring an exponentially smoothed forecast. Three conclusions are drawn. First, previously used performance measures are inadequate. As a consequence, currently available control limits can give false alarm rates that are substantially different than advertised. Second, two commonly used tracking signals can be substantially improved by choice of smoothing parameters. Finally, when measured by the new criteria proposed in this paper, the smoothed error tracking signal is substantially better than the unweighted sum of errors (CUSUM) method. |
