ABSTRACT
Time series are encountered frequently in diverse settings. One is often interested in time series for either of two important purposes: (a) to distinguish (discriminate) systems, based on statistical characteristics, or (b) to mathematically model systems. In both cases, effective statistics and models need to account for the sequential interrelationships among the data—study of autocorrelation and of power spectra is motivated by this recognition. In this chapter we focus on the first of these purposes, statistical discrimination, via a quantification of regularity of a time series. This approach also calibrates the extent of sequential interrelationships, from a relatively new perspective, based on quantifying a notion of orderliness (as opposed to “randomness”) of the data.
