Indexing of Compressed Time Series
Document Type
Article
Publication Date
2004
Keywords
Time series, compression, fast retrieval, similarity measures
Digital Object Identifier (DOI)
https://doi.org/10.1142/9789812565402_0003
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 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, and electroencephalograms.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Data Mining in Time Series Databases, p. 43-65
Scholar Commons Citation
Fink, Eugene and Pratt, Kevin B., "Indexing of Compressed Time Series" (2004). Computer Science and Engineering Faculty Publications. 131.
https://digitalcommons.usf.edu/esb_facpub/131
