Data Mining for Prediction. Financial Series Case

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.