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410]. Nevertheless, this model is not applicable to interesting trading strategies. It does not produce ups and downs needed for developing those trading strategies. This model has zero difference for all days. Parameters for the most successful Models 4 and 5 were discovered using the relational data mining approach and the MMDR algorithm described in Chapters 4 and 5. 7) we presented comparative capabilities of different data mining methods based on [Dhar, Stein, 1997]. According to Dhar and Stein, accuracy, explainability, response speed, and scalability of statistical methods including ARIMA are medium in comparison with other methods.

Schematic supervised attribute-based data mining model The aim is to find a rule (model) J predicting target the value of the target function Y(w). For example, consider w with unknown value Y(w) but with the state of all its attributes X(w) known: where J(X(w)) is a value generated by rule J. It should be done for a majority of examples w in W. 4. The choice of a specific data mining method to learn J depends on many factors discussed in this book. The resulting model J can be an algebraic expression, a logic expression, a decision tree, a neural network, a complex algorithm, or a combination of these models.

However, for non-stationary training data this is the way to “extend” the sample. Extending the data allows for a better testing of a discovered regularity. In this way, 10 attributes and 1000 cases can be used to obtain values for backcasting, because for the training data any value of the attributes, not only the target, can be backcast. This approach has been used successfully with the AVO instance-based learning method for classification of court decisions with a scarce data set [Kovalerchuk et al, 1977].

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