TIMESERIES Procedure

Singular Spectrum Analysis

Singular spectrum analysis (SSA) is a technique for decomposing a time series into additive components and categorizing these components based on the magnitudes of their contributions. SSA uses a single parameter, the window length, to quantify patterns in a time series without relying on preconceived notions about the structure of the time series. The window length represents the maximum lag considered in the analysis and corresponds to the dimensionality of the PCA (principle components analysis) on which the SSA is based.

In addition to SSA output options, an SSA statement has been added to explicitly control the window length parameter and the grouping of SSA series components.

Fourier Spectrum Analysis

Functionality similar to that available in PROC SPECTRA for analyzing periodograms of time series data has been incorporated into PROC TIMESERIES. Now ODS graphical representations of periodograms and spectral density estimates can be computed and displayed.

Database Accumulation

For Teradata-based input data sets, aggregation and accumulation can be performed using native facilities in the database server. Most ACCUMULATE= options specified in the ID and VAR statements can be performed by the database server.