Calculating ratio of sample data used for model fitting/training and validation Provided a sample size "N" that I plan on using to forecast data. What are some of the ways to subdivide the data so that I use some of it to establish a model, and the remainder data to validate the model?
I know there is no black and white answer to this, but it would be interesting to know  some "rules of thumb" or usually used ratios. I know back at university, one of our professors used to say model on 60% and validate on 40%.
 A: Well as you said there is no black and white answer. I generally don't divide the data in 2 parts but use methods like k-fold cross validation instead. 
In k-fold cross validation you divide your data randomly into k parts and fit your model on k-1 parts and test the errors on the left out part. You repeat the process k times leaving each part out of fitting one by one. You can take the mean error from each of the k iterations as an indication of the model error. This works really well if you want to compare the predictive power of different models. 
One extreme form of k-fold cross validation is the generalized cross validation where you just leave out one data point for testing and fit the model to all the remaining points. Then repeat the process n times leaving out each data point one by one. I generally prefer k-fold cross validation over the generalized cross validation ... just a personal choice
A: It really depends on the amount of data you have, the specific cost of methods and how exactly you want your result to be.
Some examples:
If you have little data, you probably want to use cross-validation (k-fold, leave-one-out, etc.) Your model will probably not take much resources to train and test anyway. It are good ways to get the most out of your data
You have a lot of data: you probably want to take a reasonably large test-set, ensuring that there will be little possibility that some strange samples will give to much variance to your results. How much data you should take? It depends completely on your data and model. In speech recognition for example, if you would take too much data (let's say 3000 sentences), your experiments would take days, as a realtime factor of 7-10 is common. If you would take too little, it is too much dependent on the speakers that you are choosing (which are not allowed in the training set).
Remember also, in a lot of cases it is good to have a validation/development set too!
A: 1:10 test:train ratio is popular because it looks round, 1:9 is popular because of 10-fold CV, 1:2 is popular because it is also round and reassembles bootstrap. Sometimes one gets a test from some data-specific criteria, for instance last year for testing, years before for training. 
The general rule is such: the train must be large enough to so the accuracy won't drop significantly, and the test must be large enough to silence random fluctuations.
Still I prefer CV, since it gives you also a distribution of error.
A: As an extension on the k-fold answer, the "usual" choice of k is either 5 or 10. The leave-one-out method has a tendency to produce models that are too conservative. FYI, here is a reference on that fact: 
Shao, J. (1993), Linear Model Selection by Cross-Validation, Journal of the American
Statistical Association, Vol. 88, No. 422, pp. 486-494
