Consensus clustering of time series data

Yetere Kurşun, Ayça
In this study, we aim to develop a methodology that merges Dynamic Time Warping (DTW) and consensus clustering in a single algorithm. Mostly used time series distance measures require data to be of the same length and measure the distance between time series data mostly depends on the similarity of each coinciding data pair in time. DTW is a relatively new measure used to compare two time dependent sequences which may be out of phase or may not have the same lengths or frequencies. DTW aligns two time series data so that the distance between them is minimized. However, DTW is a similarity measure that is employed for single variable with standard clustering methods rather than consensus clustering. Thus our motivation is to create an algorithm that can combine the benefits of the DTW with benefits of consensus clustering, which will also provide a solution for multivariate applications. We present the results of our study both with simulated data, well known datasets from the literature and Turkey’s long-term meteorological time series data between years 1950 and 2010. In all the cases we experimented with, when used with consensus clustering DTW performs better than Euclidian Distance measure. However in some cases the performance difference was insignificant, making it unnecessary to use both DTW and Consensus Clustering, due to time consuming computations. This thesis ends with a conclusion and the outlook to future studies.


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Citation Formats
A. Yetere Kurşun, “Consensus clustering of time series data,” M.S. - Master of Science, Middle East Technical University, 2014.