Thinking about a supposedly simple but interesting problem, I'd like to write some code to forecast consumable I'll need in the near future given the full history of my previous purchases. I'm sure this kind of problem has some more generic and well studied definition (someone suggested this is related to some concepts in ERP systems and the like).
The data I have is the full history of previous purchases. Let's say I'm looking at paper supplies, my data looks like (date, sheets):
2007-05-10 500 2007-11-11 1000 2007-12-18 1000 2008-03-25 500 2008-05-28 2000 2008-10-31 1500 2009-03-20 1500 2009-06-30 1000 2009-09-29 500 2009-12-16 1500 2010-05-31 500 2010-06-30 500 2010-09-30 1500 2011-05-31 1000
it's not 'sampled' at regular intervals, so I think it doesn't qualify as a Time Series data.
I have no data on the actual stock levels every time. I'd like to use this simple and limited data to predict how much paper I'll need in (for example) 3,6,12 months.
So far I came to know that what I'm looking for is called Extrapolation and not much more :)
What algorithm could be used in such a situation?
And what algorithm, if different from the previous one, could also take advantage of some more data points giving the current supply levels (e.g., if I know that on date X I had Y sheets of paper left)?
Please feel free to edit the question, title and tags if you know a better terminology for this.
EDIT: for what it's worth, I will be trying to code this in python. I know there are lots of libraries that implement more or less any algorithm out there. In this question I'd like to explore the concepts and the techniques that could be used, with the actual implementation to be left as an exercise to the reader.