TD_DIFF

The Teradata TD_DIFF - Difference and Seasonal Difference function; enables you to perform both status-quo time series differencing, seasonal based differencing, and multiplicative transforms to transform your likely non-stationary time series into a differenced time series - and thus the output of this transform function is always a new time series.

Options

DIFFERENCES: Zero or Positive Integer value. Used in formula as (1-B^LAG)^DIFFERENCES * Y_t
LAG: Zero or Positive Integer value. References, relative to any time series element, Y_t, another time series element located at Y_{t-LAG}.
OutputFormat: Specifies the INDEX_STYLE of the output format.
SEASONAL_MULTIPLIER: Zero or Positive Integer value. Compute diff according to if SEASONAL_MULTIPLIER is 0 then result is (1-B^LAG)^DIFFERENCES * Y_t, otherwise multiply same result by (1-B)^SEASONAL_MULTIPLIER
Output Schema: Output Schema, if Volatile is true then use user login as the schema.
Output Table: Output Table
VAL Location: VAL Location
Volatile: Specifies whether the table should be a VOLATILE table. If true, then the table is automatically deleted, otherwise it is users responsibility to remove or clean it up for space.

Input Ports

: Connection to a Teradata Database Instance
: The TD_DIFF function can either be passed a single time series (SERIES_SPEC(CONTENT(REAL)) as input, or can be passed in a multivariate time series (SERIES_SPEC(CONTENT(MULTIVAR_REAL)) . When passed in a multivariate series, the TD_DIFF function is executed separately against each identified series in the collection and produce a coalesced multivariate style analytical result set (OUTPUT_FMT(CONTENT(MULTIVAR_REAL)).

Output Ports

: output of TD_DIFF

Nodes

String Configuration6 ×
Python Script2 ×
Boolean Configuration1 ×
Component Input1 ×
Component Output1 ×
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TD_​DIFF

Options

Input Ports

Output Ports

Nodes

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TD_DIFF