After figuring out some of the SVM parameters, I did a comparison of an MLP (feedforward neural network) technique vs. the SVM (support vector regression) technique for use as a predictor. The data were split into train/test set at a ratio of 9/1, both the SVM and the MLP were trained with those data and this was repeated a few (20) times. It turns out that the neural network seems to perform better and oscillates less over the trial runs. The following figures tell the tale more precisely:
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