Classification and clustering of time series

Classification and clustering of time series is becoming an important area of research in several fields such as economics marketing business finance medicine biology physics psychology zoology and many others. For example in economics we may be interested in classifying the economic situation of a country by looking at some time series indicators such as Gross National Product disposable income unemployment rate or inflation rate. In this book we propose new measures of distance between time series based on the autocorrelations partial and inverse autocorrelations and periodogram ordinates. The use of both hierarchical and nonhierarchical clustering algorithms is considered. We also introduce time and frequency domain based metrics for classification of time series with unequal lengths. As economic applications we present two illustrative examples. The first uses economic time series data to identify similarities among industrial production series in the United States. The second applies the interpolated periodogram based method for classifying time series with unequal lengths of industrialized countries.