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| Excerpt | ||
|---|---|---|
| ||
aggregates data by summing the values |
Accumulative
Trim Output
- Note that this optional and default false, but very recommended to use to prevent overwriting existing values by missings (see logWarnExistingValueOverwrittenByMissing log messages). This problem will happen in case the transformation is running at a smaller cardinal timestep than the aggregation timestep. For example, running at 00:05, while aggregating to clock hours will result in a non-full clock hour at the start of the aggregation, so result in a missing value. With the trimOutput=true, that missing value will be ignored.
Input
- inputVariable. (Be careful with using non equidistant input data, this transformation is not designed for that and could give unexpected output values. In case of non-equidistant data the Statistics Serial Sum Transformation might be more suitable)
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The table below shows an example of accumulating 6-hourly values to daily values using this transformation.
| Original series | Result | |
Date/Time | Value | Value | |
01-01-2007 00:00 | 1.00 |
| |
01-01-2007 06:00 | 2.00 |
| |
01-01-2007 12:00 | 3.00 | ||
01-01-2007 18:00 | 4.00 |
| |
02-01-2007 00:00 | 5.00 | 14.00 | |
02-01-2007 06:00 | 6.00 | ||
02-01-2007 12:00 | NaN | ||
02-01-2007 18:00 | 8.00 |
| |
03-01-2007 00:00 | 9.00 | NaN | |
03-01-2007 06:00 | 10.00 |
The figure below shows original 15 minute data and the aggregated hourly data using the accumulative function:
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