TABLE OF CONTENTS In recent years, there has been an explosion of interest in mining time series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time series data. A variety of algorithms have been proposed to obtain this representation, with several algorithms having been independently rediscovered several times. In this chapter, we undertake the first extensive review and empirical comparison of all proposed techniques. We show that all these algorithms have fatal flaws from a data mining perspective. We introduce a novel algorithm that we empirically show to be superior to all others in the literature.
Keywords: Time series; data mining; piecewise linear approximation; segmentation; regression.
In recent years, there has been an explosion of interest in mining time series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. Several high level representations of time series have been proposed, including Fourier Transforms [Agrawal et al. (1993), Keogh et al. (2000)], Wavelets [Chan and Fu(1999)], Symbolic Mappings [Agrawal et al.
