期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 50, 期 10, 页码 2668-2684出版社
ELSEVIER
DOI: 10.1016/j.csda.2005.04.012
关键词
autocorrelation function; classification; clustering; Euclidean distance; periodogram; stationary and non-stationary time series
The statistical discrimination and clustering literature has studied the problem of identifying similarities in time series data. Some studies use non-parametric approaches for splitting a set of time series into clusters by looking at their Euclidean distances in the space of points. A new measure of distance between time series based on the normalized periodogram is proposed. Simulation results comparing this measure with others parametric and non-parametric metrics are provided. In particular, the classification of time series as stationary or as non-stationary is discussed. The use of both hierarchical and non-hierarchical clustering algorithms is considered. An illustrative example with economic time series data is also presented. (C) 2005 Elsevier B.V. All rights reserved.
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