کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
416218 | 681302 | 2006 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A periodogram-based metric for time series classification
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 50, Issue 10, 20 June 2006, Pages 2668–2684
Journal: Computational Statistics & Data Analysis - Volume 50, Issue 10, 20 June 2006, Pages 2668–2684
نویسندگان
Jorge Caiado, Nuno Crato, Daniel Peña,