Sunday, February 20, 2011

Stability and unsuitability for simplistic model

The main problem related to simplistic is the "stability" against training variances.

When training set suffers a little change (for instance 10 minutes more or less), the results are unpredictable, incurring into great loses or earnings…. apparently randomness (is chaotic, now I remember!)

I've tried to solve it using a linear LSSVM. With a RBF LSSVM, stupid results are got (everything has the same value per day, depending only on the set of train data… mmmmm….. it would be interesting….). With polynomial kernel, better stability and no loss in precision is got. Ok, let's try with LSSVM and polynomial kernels.

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