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

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Connection to a Teradata Database Instance
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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

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output of TD_DIFF

Nodes

Extensions

Links

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On NodePit since: 2023-12-01
Last update: 2024-05-02
Created with KNIME version: v4.7.0

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