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Asked: 2026-05-16T11:11:13+00:00

I will try to explain what I want to accomplish. I am looking for

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I will try to explain what I want to accomplish. I am looking for an algorithm or approach, not the actual implementation in my specific system.

I have a table with actuals (incoming customer requests) on a daily basis. These actuals need to be “copied” into the next year, where they will be used as a basis for planning the amount of requests in the future.
The smallest timespan for planning, on a technical basis, is a “period”, which consists of at least one day. A period always changes after a week or after a month. This means, that if a week is both in May and June, it will be split in two periods.

Here’s an example:

2010-05-24 – 2010-05-30 Week 21 | Period_Id 123
2010-05-31 – 2010-05-31 Week 22 | Period_Id 124
2010-06-01 – 2010-06-06 Week 22 | Period_Id 125

We did this to reduce the amount of data, because we have a few thousand items that have 356 daily values. For planning, this is reduced to “a few thousand x 65” (or whatever the period count is per year). I can aggregate a month, or a week, by combining all periods that belong to one month. The important thing about this is, I could still use daily values, then find the corresponding period and add it there if necessary.

What I need, is an approach on aggregating the actuals for every (working)day, week or month in next years equivalent period. My requirements are not fixed here. The actuals have a certain distribution, because there are certain deadlines and habits that are reflected in the data. I would like to be able to preserve this as far as possible, but planning is never completely accurate, so I can make a compromise here.

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1 Answer

  1. Editorial Team

    Don’t know if this is what you’re looking for, but this is a strategy for calculating the forecasts using flexible periods:

    First define a mapping for each day in next year to the corresponding day in this year. Then when you need a forecast for period x you take all days in that period and sum the actuals for the matching days.

    With this you can precalculate every week/month but create new forecasts if the contents of periods change.

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