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I am working with weekly data and I am running into troubles when analyzing them because of their inconsistent nature. By inconsistent nature, I mean that not every month is made of the same number of weeks so that some weeks span over two subsequent months and the same holds for months over subsequent years.

Just to give you an example let's suppose I have a week beginning on March 28 ending on April 3. And let's suppose I have just a figure for the whole week, while I should have two figures, one for each month.

The purpose of this dataset is to run some descriptive analysis, thus I need to be able to analyze data by weeks, (aggregated) by months and (aggregated) by years. And I would like to aggregate them properly, e.g. deaths across two months should be (as much as possible) imputed to each month correctly.

How would you proceed? Any technique better than the others?

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  • $\begingroup$ Weekly data that correspond to measurements or reports made or issued on one day of the week are in my experience best handled as daily data spaced 7 days apart and woe betide you if your software doesn't support that. At worst take the daily date (as days from some origin) and divide by 7 to get weekly dates that are successive integers. $\endgroup$ – Nick Cox May 2 '18 at 13:42
  • $\begingroup$ If you need for some unstated reason to aggregate to months (and do avoid that unless under compulsion) then averaging the weeks that notionally fall in a given month may be adequate when averages are needed but splitting weeks between months is likely to be required when totals are needed. So you need to say that you have figures. What are they and what kind of aggregation is sought? $\endgroup$ – Nick Cox May 2 '18 at 13:43
  • $\begingroup$ @NickCox nicely said !....For some of my discussions on daily data I recommend that he see stats.stackexchange.com/search?q=user%3A3382+daily+data . A major issue is often holiday effects and quite often particular days-of-the-month and even week-of-the-month besides the obvious monthly effects. In answer to the op you need to focus on determinstic effects and treat any autoregressive (memory ) effects with a seasonal (7) arima structure. $\endgroup$ – IrishStat May 2 '18 at 13:50
  • $\begingroup$ Above: for " So you need to say that you have figures" read please "So you need to say more than that you have figures" $\endgroup$ – Nick Cox May 2 '18 at 13:59
  • $\begingroup$ @NickCox I tried to better explain my case. Briefly, figures are deaths and I need to aggregate them by month and years. $\endgroup$ – PhDing May 2 '18 at 16:35

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