Another classical approach to transforming a time series from an irregular to regular, and/or altering the sampling interval associated with the time series, is to apply a resampling function to the series. Resampling functions generally take as an input: a time series, which can be regular or irregular to begin with; a starting point, aka, 'time-zero'; and a desired target sampling interval. There are usually multiple choices available to the data scientist as to how the interpolation associated with the sample interval change is going to occur.
Options
Input Ports
- Connection to a Teradata Database Instance
- The Teradata rendition of this function takes a logical series, containing any form of regular or irregular series - time series, spatial series, arbitrary numerical series - and applies an interpolation algorithm to transform it into a discrete series with the target start point and target sampling interval. The input series and the result series both contain real numbers - CONTENT(REAL) or CONTENT(MULTIVAR_REAL)- as their series elements. In producing the output, the data scientist has a choice of preserving or replacing the series indexing mechanism, via the OUTPUT_FMT(INDEX_STYLE()) declaration. The default INDEX_STYLE declaration indexes the result series as: OUTPUT_FMT(INDEX_STYLE(FLOW_THROUGH)).
Nodes
- String Configuration8 ×
- Double Configuration2 ×
- Single Selection Configuration2 ×
- Boolean Configuration1 ×
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Extensions
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