Time Series Analysis Startup Panel - Missing Data Tab

Select the Missing data tab of the Time Series Analysis Startup Panel to access the options described here.

Element Name Description
Replace missing data with Use the options in the Replace missing data with group box to specify how missing data is replaced. Practically all time series analyses require that all data are observed and that there are no "holes" with missing data in the time series. As long as the missing data are at the end of the series (trailing missing data) or the beginning of the series (leading missing data), the missing data will simply be ignored. Missing data embedded in the series have to be replaced in some way. Time Series offers a range of different methods, described here, for handling this missing data.
Overall mean If you select this option button, all missing data is simply replaced by the overall mean of the series. Very often, when the series is not stationary or when there are large systematic fluctuations in the values of the series, this method may not be appropriate. On the other hand, the overall mean is often the best a priori (unbiased) guess for the missing data.
Interpolation from adjacent points If you select this option button, the missing data is computed by interpolation from the adjacent non-missing points. Graphically, this method amounts to replacing missing data by connecting with a straight line the point just prior to the missing data with the point just following the missing data. This method, in a sense, assumes that there is some serial correlation in the data, that is, that each observation is to some extent related to and therefore most similar to the previous observation.
Mean of N adjacent points; N If you select this option button, the missing data is computed from the mean of the N adjacent points on both sides of the "hole" of missing data. For example, when N is left at its default value of 1, then missing data will be replaced by the average of the value just prior to the missing data and the value immediately following the missing data. In general, this method implies that the data in the region or window specified by the N parameter are more similar to each other than points that are further away.
Median of N adjacent points; N Select this option button to handle the missing data essentially the same as that described above, except that missing data are replaced by the median of the N non-missing adjacent point.
Predicted values from linear trend regression Select this option button to instruct STATISTICA to fit a least-squares regression line to the time series. The missing data is then replaced by the values predicted by this regression line. This method implies that the most salient (or strongest) feature of the series is its linear trend across time.
Largest absolute value for data (on reading); 1e+ Specify here the maximum absolute valid data value; absolute data values that are larger than this value will be treated as missing data. Note that the integer value k specified here will be used to compute the maximum absolute valid data value as 10k.