Date of Original Version
Journal of Experimental & Theoretical Artificial Intelligence
This is the accepted version of the article which has been published in final form at http://dx.doi.org/10.1080/0952813X.2010.505800
Abstract or Description
We formalize the notion of important extrema of a time series, that is, its major minima and maxima; analyze basic mathematical properties of important extrema; and apply these results to the problem of time-series compression. First, we define numeric importance levels of extrema in a series, and present algorithms for identifying major extrema and computing their importances. Then, we give a procedure for fast lossy compression of a time series at a given rate, by extracting its most important minima and maxima, and discarding the other points
Journal of Experimental & Theoretical Artificial Intelligence, 23, 23, 255-270.