Catching the drift : when regimes change over time

Abstract

The goal of this thesis is the identification of relationships between data streams. These relationships may change over time. The research contribution is to solve this problem by combining two data mining fields. The first field is the identification of such relationships, e.g. by using correlation measures. The second field covers methods to deal with the dynamics of such a system which require model reconsideration. This field is called “concept drift” and allows to identify and handle new situations. In this thesis two different approaches are presented to combine these two fields into one solution. After that, these two approaches are assessed on synthetic and real-world datasets. Finally, the solution is applied to the finance domain. The task is the determination of dominant factors influencing exchange rates. Finance experts call such a dominant factor “regime”. These factors change over time and thus, the problem is named “regime drift”. The approach turns out to be successful in dealing with regime drifts.