INDEXING OF COMPRESSED TIME SERIES
Eugene Fink, Kevin Pratt · Series in machine perception and artificial intelligence · 2004
Abstract. 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 in-dexing of compressed series by their major extrema, and retrieval of se-ries similar to a given pattern. The retrieval procedure searches for the series whose compressed representation is similar to the compressed pat-tern. It allows the user to control the trade-off between the speed and ac-curacy of retrieval. We show the effectiveness of the compression and re-trieval for stock charts, meteorological data, and electroencephalograms.