From CS294-10 Visualization Sp11

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Krishna Janakiraman


Existing time series exploration techniques enable exploration via zooming the axes, drawing query contours, via techniques such as generalized selection, by bounding boxes and by building augmented suffix trees. However, these techniques do not seem to show the existence of recurring patterns at varying resolutions within the time series.

For example, a given 'U' or a downward slope shaped query pattern could be present in the time series in a stretched form and can be composed of smaller similarly shaped patterns. As another example, suppose a user wants to identify a sequence of events in a time series, the matching pattern could be a contiguous sequence of events or sequence of events interspersed with other unrelated events. At a higher resolution, certain classes of the latter pattern could be relevant for the user.

The main research questions I would like to investigate are: how can patterns {shapes, sequences and conditions} be identified across varying resolutions in a time series, how such patterns can be both relaxed and constrained to allow greater flexibility and finally how the results can be summarized and visualized.

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