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.