Predictor offers three methods for detecting outliers, or significantly extreme values:
In each case, the difference is calculated between historical data points and values calculated by the various forecasting methods. These differences are called residuals. They can be positive or negative depending on whether the historical value is greater than or less than the smoothed value. Various statistics are then calculated on the residuals and these are used to identify and screen outliers.
A certain number of values must exist before the data fit can begin. If outliers appear at the beginning of the data, they are not detected.
Note: | Time-series data is typically treated differently from other data because of its dynamic nature, such as the pattern in the data. A time-series outlier need not be extreme with respect to the total range of the data variation but it is extreme relative to the variation locally. |