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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.
About the author
Ph.D. in Mathematics Applied in Economics and Management (ISEG, Technical Univerisity of Lisbon). Pos-doc researcher at the Centre for Applied Mathematics and Economics (CEMAPRE, Lisbon). Invited Professor at New University of Lisbon (ISEGI). He is author of several papers in peer-reviewed journals and international conferences.
