Your explanation is too confusing.
1. Timeseries analysis contains one basic assumption:
Every contiguous subset of the timeseries has the same approximate summary statistics of mean, standard deviation and correlation coefficients.
2. If you use DIVIDEBLOCK, you do not have to worry about causality or training with nontraining data. Using the default 0.7/0.15/0.15 ratios, the 2nd (validation) and third (testing) blocks each contain Ntst = Nval = floor(0.15*N) points of NONTRAINING DATA. They are not used for weight estimation.
However, the val block will stop training if it's error rate increases continually for 6 epochs. Therefore, although it is not part of the training set, it can be considered a part of the design subset. i.e.,
data = training + nontraining
nontraining = validation + testing
data = design + nondesign
nondesign = testing
Since the val subset is not directly involved in weight estimation, it's slightly biased error rate is not a bad estimate of net performance on all nondesign data.
Finally, the test subset yields a completely unbiased estimate of net performance.
Unfortunately, many timeseries do not have stationary summary statistics and multiple nets are required to model the series over it's entire length.
Again, the validation subset performance is the best indicator that the assumption of stationarity is becoming invalid.
Therefore, use divideblock and don't worry unless plots or other evidence show that the series is not approximately stationary.
Hope this helps.
*Thank you for formally accepting my answer.
Greg
