Interactive Pattern Search in Time Series (Using TimeSearcher 2) Paolo Buono, Aleks Aris, Catherine Plaisant, Amir Khella, and Ben Shneiderman Proceedings, Conference on Visual Display and Analysis, 2005 Presented to CMSC 838S (Information Visualization) By Derek Juba & Scott Nestler On Feb. 16, 2006

Outline Introduction Related Work TimeSearcher 2 Demo Key Contributions Future Work Conclusions

Introduction Time series- sequence of real numbers, representing observations of variable at equal time intervals Many applications of time series (e.g. EKGs, seismographs, digital recordings) Traditional analyses are based in classical statistics Now able to explore time series data with visualization tools TimeSearcher 2 designed for range of users; statistical analysis skills not required

Introduction (cont) Timebox- rectangular region that is selected and directly manipulated on a timeline overview of data –Boundaries of timeboxes used to specify parameters for query –Two types Original- used to filter data and reduce scope of search New- used to perform specific pattern search in remaining data

Related Work Diamond Fast (Unwin & Wills, 1999) –Allows visualization, moving, resizing –Only manages short time series ILOG & Personal Stock Monitor –High level of interaction with enhanced zoom –Lack search capabilities; limited to a single time series Semantic zoom (Brodbeck, Girardin, 2003) Spiral view(Carlis & Konstan, 1998)

Related Work (cont) Time Searcher 1- patterns specified with timeboxes Choratas- pattern specified numerically VizTree- pattern specified in segments QuerySketch- allows direct sketching of pattern IPBC- 3D tool allows selecting pattern in data; for periodic data No known tool other than TimeSearcher allows specifying multiple patterns on multiple variables

TimeSearcher 2 Demo Demo

Key Contributions Three-step interactive search 1.Reduce scope with timebox(es) 2.Specify a pattern to search for 3.Refine query dynamically Search algorithm –Uses modified Euclidean distance between two time series –Transformations available Offset translation Magnitude scaling Linear trend removal Noise reduction

Future Work Dealing with larger datasets Improved interaction –Search pattern selection Dealing with missing data –Ignore? –Estimate? Evaluation

Conclusions Room for improvement in interactive exploration of time series –Improved algorithms –Interactive interfaces Interactive search used with traditional features aids exploratory analysis Three-step framework applicable to larger data archives