Hard problems force innovative approaches and attention to detail, their exploration often contributing beyond the area initially attempted. This thesis investigates the data mining process resulting in a predictor for numerical series. The series experimented with come from financial data - usually hard to forecast.One approach to prediction is to spot patterns in the past, when we already know what followed them, and to test on more recent data. If a pattern is followed by the same outcome frequently enough, we can gain confidence that it is a genuine relationship.Because this approach does not assume any special knowledge or form of the regularities, the method is quite general - applicable to other time series, not just financial.However, the generality puts strong demands on the pattern detection - as to notice regularities in any of the many possible forms.

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