Best approach: transfer daily values from one year to another

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 a开发者_StackOverflown 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.

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.

1. Map weeks to weeks. The first full week of this year to the first full week of the next. Don't worry about "periods" and aggregation; they are irrelevant.
2. Where a missing holiday leaves a hole in the data, just take the values for the same day of the previous week or the next week, and do the same at the beginning/end of the year.
3. Now for each day of the week, combine the results for the year and look for events more than, say, two standard deviations from the mean (if you don't know what that means then skip this step), and look for correlations with known events like holidays. If a holiday doesn't show an effect in this test then ignore it. If you find an effect, shift it to compensate for the different date next year. Don't worry about higher-order effects, you don't have enough data to pin them down.
4. Now draw in periods wherever you like and aggregate all you want.

Don't make any promises about the accuracy of these predictions, there's no way to know it. Don't worry about whether this is the best possible way; it isn't, but it's as good as any you're likely to find. You can spend as much more time and effort fine-tuning this as you wish; it might raise expectations but it's not likely to make the results much more accurate-- it's about as likely to make them worse.

There is no A-priori way to answer that question. You have to look at your data, and decide what the important parameters (day of week, week number, month, season, temperature outside?) using the results.

For example, if many of your customers are jewish/muslim, then the gregorian calendar, and ISO-week numbers and all that won't help you much, because jewish/muslim holidays (and so users behaviour) are determined using other calendars.

Another example - Trying to predict iPhone search volume according to last year's search doesn't sound like a good idea. It seems that the important timescales are much longer than a year (the technology becoming mainstream over the years) and much shorter than a year (Specific events that affect us for days-weeks).