Techniques for forecasting short-frame data?

Question

I'm having a problem in which a time series of 24 data points is given to forecast the next 12 data points. This 24 data points might be sparse (many are missing).

Do you have any suggestion on what technique can be used?

Thanks,

ADDED: The series is a transformed data from tabulated dataset which shows a measure of one's `skill' over time. The dataset given is only capture in the previous 2 years on a monthly basis, I need to find either an estimation of his/her skill over the next 12 months, or some average measure is also aceptable.

Answer 1

In order to properly answer this question, I would ask that you also tell us:

1. Is the "skill" variable continuous or discrete? How is it actually measured?
2. Can you put theoretical bounds on the skill? Is it, say, between 0 and 1, or in some well-defined range?
3. Do you expect skill to change over the next 12 months? Is the person being measured continuing to put effort into improving his skill over time? Does skill tend to mean-revert?
4. Do you have any other variables which may reasonably be correlated with skill?

Until you have answered these questions, my provisional answer is that you should regress the skill on a constant, a lag, and a time trend. I also agree with @QuantGuy's recommendation that you plot the data to see if there are any obvious patterns such as trending or mean-reversion. You may, for example, see that skill appears to be deteriorating, in which case you may want to estimate the rate of decay using a regression of log skill on time. Proper estimation of the dynamics of the process will help you determine both what the skill will be in the immediate future and what it will be in one year's time.