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Time series aggregations in scorecards and widgets

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Time series aggregations in scorecards and widgets

In scorecards and some widgets, you can apply a time series aggregation to the scores, such as a monthly average. The frequency with which scores are collected for the indicator determines which time series are applicable. Some time series include data from partial collection periods.

Table 1. Time series and associated indicator frequencies
Time series Includes partial periods? Indicator frequency
Daily No Daily
28d running No Daily
30d running No Daily
7d running No Daily
Fiscal quarter to date No Daily
Fiscal year to date No Daily
Month to date No Daily
Quarter to date No Daily
Week to date No Daily
Year to date No Daily
12m running No Monthly
3m running No Monthly
6m running No Monthly
By month No Monthly
By month + Yes Monthly
4q running No Quarterly
By fiscal quarter No Quarterly
By fiscal quarter + Yes Quarterly
By quarter No Quarterly
By quarter + Yes Quarterly
13w running No Weekly
4w running No Weekly
By week No Weekly
By week + Yes Weekly
By fiscal year No Yearly
By fiscal year + Yes Yearly
By year No Yearly
By year + Yes Yearly

Partial periods

Some time series include indicator scores from incomplete collection periods. These periods can include the current period and the period from the beginning of data collection. A plus sign in the name, +, identifies these time series.

A time series that does not include data from partial periods must have data from the beginning and the end of the period. For example, a By month time series requires scores from the start and the end of the month to be present. Otherwise that month is not included. A time series that includes data from partial periods, such as By month +, needs only data from one day in the period.

Warning: Partial periods can skew the results of certain aggregations, such as averages.

Default time series definitions

Performance Analytics comes with default SUM, AVG, and other time series definitions. Do not alter these definitions.

Warning: Any changes to time series definitions can have unexpected results.