A Study on Time Series Clustering
In recent years, the topic of classification which is advantageous in terms of time and cost is of great interest in various fields. Especially, when a large number of series it is much more practical to classify the series into similar groups and to make an estimate for each corresponding group rather than to make prediction for every given series individually. For this reason, some studies have been carried out in order to develop various classification and clustering methods by using characteristics of time series. In this study, model based approaches: Maharaj’s p-value based distance, Piccolo’s AR distance, Cepstral based distance and free model based methods: Autocorrelation based distance, Chouakria-Douzal dissimilarity measure, Minkowski distance are compared in terms of clustering performances of time series. Also, the performances of the clustering methods are investigated for different ranking of the processes and correlation structures among the series. In the result of the study, it is obtained that Maharaj’s p-value based distance is the best method regarding to clustering performance and Piccolo’s AR distance based clustering is the least affected method by the different ranking of the processes.
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