Publication: Time series segmentation by Cusum, AutoSLEX and AutoPARM methods
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2009-12
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Abstract
Time series segmentation has many applications in several disciplines as neurology,
cardiology, speech, geology and others. Many time series in this fields do not behave as
stationary and the usual transformations to linearity cannot be used. This paper
describes and evaluates different methods for segmenting non-stationary time series.
We propose a modification of the algorithm in Lee et al. (2003) which is designed to
searching for a unique change in the parameters of a time series, in order to find more
than one change using an iterative procedure. We evaluate the performance of three
approaches for segmenting time series: AutoSLEX (Ombao et al., 2002), AutoPARM
(Davis et al., 2006) and the iterative cusum method mentioned above and referred as
ICM. The evaluation of each methodology consists of two steps. First, we compute how
many times each procedure fails in segmenting stationary processes properly. Second,
we analyze the effect of different change patterns by counting how many times the
corresponding methodology correctly segments a piecewise stationary process.
ICM method has a better performance than AutoSLEX for piecewise stationary
processes. AutoPARM presents a very satisfactory behaviour. The performance of the
three methods is illustrated with time series datasets of neurology and speech.
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Time series segmentation, AutoSLEX, AutoPARM, Cusum Methods