Simple Fourier-Type Transformations

Creates various transformations related to Fourier (spectral) decomposition, including tapering, various smoothing methods (Daniell, Tukey, Hamming, Parzen, Bartlett), the real and imaginary parts of the series, as well as the inverse transformation. See also the Single Series Fourier Analysis and Two Series Fourier Analysis facilities for a complete set of options for spectral analysis.

General

Element Name Description
Type of transformation Select the desired type of transformation. Note that the Inverse Fourier transformation requires that a continuous (predictor) variable be specified to contain the imaginary part for the transformation (the continuous dependent variables should the real part for the final transformations).
Percent of tapered cases Specifies the percent of tapered cases; only applicable if Tapering is selected for the transformation.
Data window for smoothing Specifies the width of the smoothing window (must be odd and greater than or equal to 3); this option is only applicable for the various smoothing transformations (Daniell, Tukey, Hamming, Parzen, Bartlett).
Generates data source, if N for input less than Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.

Missing Data

Element Name Description
Replace missing data Specifies how missing data is to be replaced. Missing data can be replaced by the overall mean, interpolated from adjacent points, replaced by the mean or median of N adjacent points (on both sides of the hole), or estimated (predicted) from linear trend regression. Note that 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.
Number of adjacent points Applicable if missing data are replaced by the mean or median of N adjacent points; specify N. The missing data are replaced by the mean or median computed from the N adjacent points on both sides of the hole of missing data.