Indexing of compressed time series
Eugene Fink and Kevin B. Pratt
In Mark Last, Abraham Kandel, and Horst Bunke, editors, Data Mining in
Time Series Databases, pages 43-65. World Scientific, Singapore, 2004.
We describe a procedure for identifying major minima and maxima of a time
series, and present two applications of this procedure. The first application
is fast compression of a series, by selecting major extrema and discarding
the other points. The compression algorithm runs in linear time and takes
constant memory. The second application is indexing of compressed series
by their major extrema, and retrieval of series similar to a given pattern.
The retrieval procedure searches for the series whose compressed representation
is similar to the compressed pattern. It allows the user to control the
trade-off between the speed and accuracy of retrieval. We show the effectiveness
of the compression and retrieval for stock charts, meteorological data,