This slideshow is about the time series classifier algorithm, ShiftTree.
ShiftTree is a unique, model-based approach for time series classification. The basic idea is that we assign a cursor (or eye) to each series and move this to certain positions on the time axis. We generate dynamic attributes by answering two questions: (1) Where to look? (2) What to look at?. The answer to the first question tells us where to move the cursor (e.g.: forward 100 steps, to the previous local maxima, etc), while the second answer defines the calculation of the dynamic attributes (e.g.: value at that point, the weighted avarage of the values around the position, the difference in the current and previous cursor position, etc). These dynamic attributes then used in a binary decision tree.
This slideshow was originally presented at ECML/PKDD 2011 in Athens.
Note that for whatever reasons SlideShare doesn't support animations. Therefore the animated slides were split into multiple slides.