D. Peña
Clustering scalar time series can be carried out using their univariate properties
and hierarchical methods, especially when the dynamic structure of the series is
of interest. Two major issues in clustering analysis are to detect the existence of
multiple clusters and to determine their number, if exist. In this paper we propose
a new test statistic for detecting the existence of multiple clusters in a time-series
data set and a new procedure to determine the number when clusters exist. The
proposed method is based on the jumps, i.e., the increments, in the heights of the
dendrogram when a hierarchical clustering is applied to the data. We use parametric
bootstraps to obtain a reference distribution of the test statistics and propose an
iterative procedure to find the number of clusters. The performance of
the proposed procedure in finite samples are investigated by Monte Carlo simulations
and examples.
Keywords: Dendrogram, Distance, Gap statistic, Hierarchical clustering, Jump, Parametric bootstrap, Silhouette statistic, Similarity
Scheduled
GT03.AMC4 Clustering and Classification
November 9, 2023 3:30 PM
CC3: Room 1