Weighted average

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You can use the weighted average method in your accrual calculations for both cost and consumption values. The method uses up to 4 months of relevant seasonal historical data to derive an estimate for the missing period.

With this method, the following weights are applied to months:

A weight of 3 is applied to the month immediately before and after the gap.
A weight of 1 is applied to the same month last year and the same month before the gap last year.

The accruals are calculated as the weighted average of these 4 months.

Table 1 presents an example of how the weighted average methodology is applied to missing data in March 2023.

Table 1. Weighted average
Month with gap	Accrual calculation
March 2023	(((`Daily average of February 2023` * 3) + (`Daily average of April 2023` * 3) + (`Daily Average of March 2022` * 1) + (`Daily average of February 2022` * 1 )) / (3 + 3 + 1 + 1)) * `# of missing days in March 2023`

If actual data is not available in the relevant month, then both the daily average and the weight are treated as having a value of 0 for that month. The calculation of the accruals is based on actual data only and are not based on any other accruals.

The algorithm that the system uses incorporates a tolerance threshold to ensure that last year's data was not an anomaly. If data from last year is not within a threshold +/- 30% of the combined current year months' average, then the data from last year is not used in the accrual calculation. However, if there is no data in the current year that can be used as the reference point, then data from last year, if available, is used regardless for the accrual calculation.

If data is not available for any of the four months, then the algorithm uses the last available month, that is, the latest month that has actual data, to generate accruals. This typically happens for accounts that are missing data for more than 12 months of a period.