Abstract
Following our previous works where an improved dynamic time warping (DTW) algorithm has been proposed and motivated, especially in the multivariate case, for computing the dissimilarity between curves, in this paper we modify the classical DTW in order to obtain discrete warping functions and to estimate the structural mean of a sample of curves. With the suggested methodology we analyze series of daily measurements of some air pollutants in Emilia-Romagna (a region in Northern Italy). We compare results with those obtained with other flexible and non parametric approaches used in functional data analysis.
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Morlini, I., Zani, S. (2006). Estimation of the Structural Mean of a Sample of Curves by Dynamic Time Warping. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_5
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DOI: https://doi.org/10.1007/3-540-35978-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35977-7
Online ISBN: 978-3-540-35978-4
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