TD_GENSERIES4SINUSOIDS

In building statistical ARMA-style forecasting models, a necessary precondition is that the time series being modeled be stationary: stationary with respect to mean; stationary with respect to covariance; and, stationary with respect to variance. Quite often, the time series that the data scientist wishes to model contains a trend - meaning that the time series is non-stationary with respect to the mean; or, alternatively may contain some periodicities (cyclic variance in data). This trend and/or periodicities must be removed before modeling may begin. . Once the data scientist has devised a formula to represent the trend or periodic behaviors, their next task is to generate a series using that formula; such that they can then input both the original series and formula driven series through a pointwise subtractor function, which forms a new series with the trend and/or periodicities subtracted out.

Input ports

1. Type: DB Session
   Teradata Connection
   Connection to a Teradata Database Instance
2. Type: Table
   Input
   The input to TD_PLOT can either be a a SERIES_SPEC or MATRIX_SPEC dependent on the plot type that is specified as a parameter. TD_PLOT currently supports the SEQUENCE style of index in the ROW_AXIS or COLUMN_AXIS clauses within the SERIES_SPEC or MATRIX_SPEC.

Output ports

1. Type: Table
   output of TD_GENSERIES4SINUSOIDS
   output of TD_GENSERIES4SINUSOIDS