Adapt() or Train()


We were wondering whether the batch training (train()) or the online training (adapt()) is the better method to train the ANN. Here is an excerpt from a Matlab forum:

I would like to know what the differences are between the ADAPT and TRAIN functions.
The difference between these training methods is that ADAPT is optimized for situations where the order in which the data is presented matters. An example application would be filtering a time-based signal. TRAIN disregards the sequential order of the data, and treats the error of the entire set in each training epoch. If you use ADAPT with an input sequence of [1 10 2], you’ll see poor performance when passing in the input sequence [2 1 10]. If you use TRAIN, then you should get the same result for an input of 2 no matter its location in the sequence of inputs.

Our measurements, based on these parameters.
Input: only the continuous currDayChange. (no dayOfTheWeek input)
Target: next day change
nNeurons = 2;
nEpoch = 5;
lookbackWindowSize = 200;
outlierFixLimit = 0.04;
nEnsembleMembers = [1, 0, 0];

We made 8 random experiments. The performance measurements are here:

When using Adapt(), our most important measurements, the D_stat drops from 51.74% to 50.15%. 50% directional accuracy means, it has no predictive power at all. (A significant part of this underperformance is due to the fact that we use continuous, non discrete input.) We have 200 samples in the training set, because our lookback days is 200 and it seems that Adapt() over-emphasizes the last samples, so in our case it is not advantageous to use. We learnt a new thing today.


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