Building a forecast model based on past year data in R I am attempting to build a model to forecast attendance in a given week in the current year based on this year's attendance values up until the present, and data from two previous years. My data looks like this:
   Week 11-12 Cumulative ADA    12-13 Cumulative ADA    13-14 Cumulative ADA
   1    0.9941                  0.9941                  0.9914
   2    0.9907                  0.991                   0.989
   3    0.9888                  0.9888                  0.9879
   4    0.9877                  0.987                   0.9869
   5    0.9869                  0.9865                  0.9867
   6    0.9862                  0.985                   0.9859
   7    0.9856                  0.9842                  0.9857
   8    0.9856                  0.984                   NA
   9    0.9852                  0.9839                  NA
   10   0.9848                  0.9834                  NA

Any guidance on how to predict the three NAs based on the past two years data and this year's values would be much appreciated.
Thanks!
 A: Forecasting weekly data using weekly history is problematic as what we do in say week 4 is probably not what we did in week 4 last year whereas what we do in month 4 is probably systematic with what we did in month 4 last year ,save special effects such as Easter or Thanksgiving. Furthermore the different number of weeks in a year can throw a monkey-wrench into the analysis. More importantly the effect of holidays on weekly sums can be quite dependent on when the holiday occurs thus effectively distorting pattern. I have seen very few examples of where weekly data is consistent/predictable and can be used reliably to obtain weekly forecasts.
With the development of statistically aggressive daily models taking into account the window of response around each holiday/event, day-of-the-week effects,day-od-the-month effects,month-of-the-year effects,level shifts and or local time trends... users are now developing daily models to obtain weekly predictions. Additionally they can compute probabilities of making month-end numbers or of meeting a plan/goal number.
THe other item dealing with missing values is easily handled by Intervention Detection schemes which would identify pulses for the missing values and effectively replace the missing value with an imputed value based upon the full model. 
