Single Series Spectral (Fourier) Analysis

Performs a single series spectrum (Fourier) analysis for the specified variables (series); computes the sine and cosine coefficients, periodogram, and (smoothed) spectral density estimates using various types of weights (Daniell, Tukey, Hamming, Parzen, Bartlett).

Transform Input Series

Element Name Description
Taper input series Performs tapering of the input series prior to the spectral (Fourier) analysis; the so-called process of split-cosine-bell tapering is a recommended transformation of a series prior to spectrum analysis. It usually leads to a reduction of leakage in the periodogram.
Taper percent Specifies the percent of cases or observations at the beginning and end of the series to be used in the computations for tapering.
Subtract mean Subtracts the mean from the input series prior to the spectral (Fourier) analysis; because the goal of spectrum analysis is to detect underlying periodicity, the overall mean is usually not of interest.
Detrend input series Remove any linear trend from the input series prior to the spectral (Fourier) analysis; like the mean, an overall trend is not of interest when you want to uncover periodicities in the series. Therefore, it should be removed prior to the analysis.
Pad length of series Specifies whether the input series should be padded or truncated prior to the analysis. Note that the algorithms used in STATISTICA do not require that the length of the input series is equal to a power of 2.
Number of zeros (padding) Specifies the N number of zeros to add at the end of the input series prior to Fourier (spectral) analysis; this option is only applicable if Pad end with N zeros is selected for the Pad length of series option.
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.

Results

Element Name Description
Detail of computed results reported Specifies the level of computed results reported. At the Minimal level of detail, only the summary spreadsheet will be displayed; if Comprehensive detail is requested, various additional graphs (histograms) are computed.
Data window for density Specifies the smoothing algorithm to use for computing the spectral density estimates.
Width of data window Specifies the width for the data window for computing the spectral density estimates.
Highlights values Select this option and specify a value in the Highlight value field below to highlight large values in the summary spreadsheet. When the spreadsheet is calculated, all values in the periodogram and spectral density columns that are larger than the specified value will be highlighted.
Value for highlighting Specifies a value for highlighting large values in the summary spreadsheet; this option is only applicable if the Highlight values option is selected. When the spreadsheet is calculated, all values in the periodogram and spectral density columns that are larger than the specified value will be highlighted.
Displays largest N values Specifies whether to create a summary spreadsheet showing only the periods/frequencies with the largest N periodogram or spectral density values.
N largest values Specifies a value for N that will be used in the spreadsheet that shows only the periods/frequencies with the largest periodogram or spectral density values; this option is ignored if the Display largest N values option is set to None.
Plots by Specifies how to scale the X (horizontal) axis in plots of the periodogram, spectral densities, or sine/cosine coefficients.

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.