Masters Thesis, Computer Science and Engineering Department, University of South Florida, 2001.
We describe a technique for fast compression of time-series,
indexing of the resulting compressed series, and retrieval of series similar to
a given pattern. The compression algorithm identifies "important"
points of a time-series and discards the other points. It runs in linear time,
takes constant memory, and gives good results for a wide variety of
time-series. We use the important points not only for compression, but also for
indexing a database of time-series, which supports efficient search for
patterns and allows the user to control the trade-off between the speed and
accuracy of search. The experiments show the effectiveness of the developed
technique for identifying patterns in stock prices, meteorological data, and