I have some very complicated data about some movie sales online, first for each data entry, I have a key which is a combination of five keys, which are territory, day, etc, and then, for each key I have the sales for a period of time, and other information, like the movie's box office and genre. 

For each day, there is a delay for the data loading to the database, around ten hours, I try to fill the gap, do some data extrapolations.

For each movie we sell, there is some decay of selling since the new release of the movie, i.e. usually for each movie, it follows some sales decay pattern.

For a recent day, I pulled some data, and I found that some decay pattern:

![decay curve 1][1]


![decay curve 2][2]


![decay curve 3][3]


And for that day, the sales for each key can range from around $150000 to $0. The pic is as follow:

![one day sales curve][4] 


  [1]: https://i.sstatic.net/H0lSJ.png
  [2]: https://i.sstatic.net/L5DAf.png
  [3]: https://i.sstatic.net/RJPjK.png
  [4]: https://i.sstatic.net/o7tsq.png

In the picture, the 15000 means there are around 15000 keys for each day. 

found this article,

http://homepage.stat.uiowa.edu/~kcowles/s166_2009/Project_Lee&Pyo.pdf

And for each day, we what to come up a list of keys, and the extrapolated sales for each key. 

I am not sure whether can be applied, and how to be applied here.

Thanks a lot in advance.