I have a broad question about sliding window validation. Specifically, I am looking at using Rapid Miner to predict future values of a financial series using "lagged" values of that series and other covariates. I have been experimenting with the windowing operator in this software and lagging the values to prepare for modeling. What I am confused about, and suspect this is a general process, not just something centric to Rapid Miner and thus I ask it here, is the sliding window training/evaluation process.
Does anyone have sources to recommend for learning about sliding window processes for building data mining models on time series?
Specifically when building a model, I think I understand that k instances are used to train a model (e.g. SVM) and the performance of this model is determined by predicting the next m records. Then, the window is slid forward some amount and the next k records are used for training and the evaluation is done on the subsequent m records. This continues until the end of the data.
Is my understanding correct?
How is a final model built for use on future data? Is it always re-trained on the last k records and these last k records would only be used to create the final model?