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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Cardoso, M. G. M. S. & Martins, A. A. (2022). The Performance of a Combined Distance Between Time Series. In Regina Bispo, Lígia Henriques-Rodrigues, Russell Alpizar-Jara, Miguel de Carvalho (Ed.), Recent developments in statistics and data science. SPE 2021. (pp. 71-83).: Springer.
Exportar Referência (IEEE) 
M. M. Cardoso and A. A. Martins,  "The Performance of a Combined Distance Between Time Series", in Recent developments in statistics and data science. SPE 2021, Regina Bispo, Lígia Henriques-Rodrigues, Russell Alpizar-Jara, Miguel de Carvalho, Ed., Springer, 2022, pp. 71-83
Exportar BibTeX 
@inproceedings{cardoso2022_1732200951781,
	author = "Cardoso, M. G. M. S. and Martins, A. A.",
	title = "The Performance of a Combined Distance Between Time Series",
	booktitle = "Recent developments in statistics and data science. SPE 2021",
	year = "2022",
	editor = "Regina Bispo, Lígia Henriques-Rodrigues, Russell Alpizar-Jara, Miguel de Carvalho",
	volume = "",
	number = "",
	series = "",
	doi = "10.1007/978-3-031-12766-3",
	pages = "71-83",
	publisher = "Springer",
	address = "",
	organization = ""
}
Exportar RIS 
TY  - CPAPER
TI  - The Performance of a Combined Distance Between Time Series
T2  - Recent developments in statistics and data science. SPE 2021
AU  - Cardoso, M. G. M. S.
AU  - Martins, A. A.
PY  - 2022
SP  - 71-83
SN  - 2194-1009
DO  - 10.1007/978-3-031-12766-3
AB  - This paper presents the comparison of a proposed measure of dissimilarity between time series (COMB) with three baseline measures. COMB is a convex combination of Euclidean distance, a Pearson-correlation-based distance, a Periodogram-based measure and a distance between estimated autocorrelation structures. The comparison resorts to 1-Nearest Neighbour classifier (1NN) since the effectiveness of the dissimilarity measures is directly reflected on the performance of 1NN. Data considered is available in the University of California Riverside (UCR) Time-Series Archive which includes datasets from a wide variety of application domains and have been used in similar studies. The COMB measure shows promising results: a good trade-off performance-computation time when compared to the alternative distances considered. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ER  -