Predictor provides two methods for filling in missing values and adjusting outliers:
Missing values at the beginning of a data series are ignored. Missing values at the end of a data series are allowed, but this condition is not ideal. The cubic spline interpolation method is especially sensitive to data missing at the end of the series. If one or two values are missing, cubic spline interpolation can be used. If multiple values are missing, neighbor interpolation provides a better estimate.
Tip: | An obvious outlier, such as a large data spike, should be replaced by a blank cell in the original data set. Otherwise, neighbor interpolation is probably a better adjustment method, especially if the specified neighbors do not include the spike. Because cubic spline interpolation takes into account the whole data set, that adjustment method will be affected by the outlier. |