Common Dissimilarity Measures are Inappropriate for Time Series Clustering
Cássio M. M. Pereira, Rodrigo Fernandes de Mello · Revista de Informática Teórica e Aplicada · 2013
Clustering algorithms have been actively used to identify similar timeseries, providing a better understanding of data. However, common clustering dis-similarity measures disregard time series correlations, yielding poor results. In thispaper, we introduce a dissimilarity measure based on series partial autocorrelations.Experiments compare hierarchical clustering algorithms using the common dissimi-larity measures, such as Euclidean Distance and Dynamic Time Warping, to clustertime series following Box-Jenkins Auto-Regressive models. Results show that ourdissimilarity measure produces better results for both synthetic and real data sets interms of the Adjusted Rand Index and Normalized Hubert Γ statistic. Our findingsconfirm that the choice of dissimilarity measure is crucial for improving time seriesclustering quality.