Comparative Analysis of Selected Distance Measures Dedicated to Time Series

• Autorzy: Alicja Ganczarek-Gamrot
• Języki publikacji: EN

Abstrakty

EN

When analysing multivariate time series, we are often faced with the problem of non-uniform frequency of observations. The data from multiple sources is registered at intervals of varying length. We can solve this problem by aggregating data, losing information about the variability within in shorter periods. Taking into account additionally the non-stationary character of time series as well as time-varying correlations between them, methods allowing for the analysis of phenomena observed at different time intervals become interesting. The aim of this chapter is to identify, among the distance measures dedicated to time series, those that can be used to group multidimensional time series. Cluster analysis was carried out using the average linkage agglomeration method. The Silhouette index was used to assess the quality of the clustering.(fragment of text)

Słowa kluczowe

EN

Comparative analysis; Time-series; Statistical indicators; Cluster analysis

PL

Analiza porównawcza; Szeregi czasowe; Mierniki statystyczne; Analiza skupień

• Czasopismo: Scientific Publications / University of Economics in Katowice
• Rocznik: 2023
• Tom: Multidimensional Data Modelling and Risk Analysis
• Strony: 60--69

Twórcy

autor

Alicja Ganczarek-Gamrot

• Uniwersytet Ekonomiczny w Katowicach

Bibliografia

• Berndt D.J., Clifford J. (1994), Using Dynamic Time Warping to Find Patterns in Time Series, KDD Workshop, pp. 359-370.
• Caiado J., Crato N., Peña D. (2006), A Periodogram-Based Metric for Time Series Classification, "Computational Statistics & Data Analysis", Vol. 50(10), pp. 2668-2684.
• Douzal-Chouakria A., Nagabhushan P.N. (2007), Adaptive Dissimilarity Index for Measuring Time Series Proximity, "Advances in Data Analysis and Classification", Vol. 1(1), pp. 5-21.
• Frechet M.M. (1906), Sur Wuelques Points du Calcul Fonctionnel, "Rendiconti del Circolo Matematico di Palermo (1884-1940)", Vol. 22(1), pp. 1-72.
• Galeano P., Peña D. (2000), Multivariate Analysis in Vector Time Series, "Resenhas do Instituto de Matemática e Estatística da Universidade de São Paulo", Vol. 4(4), pp. 383-403.
• Golay X., Kollias S., Stoll G., Meier D., Valavanis A., Boesiger P. (2005), A New Correlation Based Fuzzy Logic Clustering Algorithm for fMRI, "Magnetic Resonance in Medicine", Vol. 40(2), pp. 249-260.
• Montero P., Vilar J.A. (2014), TSclust: An R Package for Time Series Clustering, "Journal of Statistical Software", November, Vol. 62, Iss. 1, pp. 1-43.
• Sankoff D., Kruskal J.B. (1983), Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison, Addison-Wesley Publishing Company.

Identyfikatory

DOI

10.22367/uekat.9788378758686