NDLI: Clustering time series from ARMA models with clipped data

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Clustering time series from ARMA models with clipped data

Content Provider	ACM Digital Library
Author	Bagnall, A. J. Janacek, G. J.
Abstract	Clustering time series is a problem that has applications in a wide variety of fields, and has recently attracted a large amount of research. In this paper we focus on clustering data derived from Autoregressive Moving Average (ARMA) models using k-means and k-medoids algorithms with the Euclidean distance between estimated model parameters. We justify our choice of clustering technique and distance metric by reproducing results obtained in related research. Our research aim is to assess the affects of discretising data into binary sequences of above and below the median, a process known as clipping, on the clustering of time series. It is known that the fitted AR parameters of clipped data tend asymptotically to the parameters for unclipped data. We exploit this result to demonstrate that for long series the clustering accuracy when using clipped data from the class of ARMA models is not significantly different to that achieved with unclipped data. Next we show that if the data contains outliers then using clipped data produces significantly better clusterings. We then demonstrate that using clipped series requires much less memory and operations such as distance calculations can be much faster. Finally, we demonstrate these advantages on three real world data sets.
Starting Page	49
Ending Page	58
Page Count	10
File Format	PDF
ISBN	1581138881
DOI	10.1145/1014052.1014061
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Time series Arma Clustering
Content Type	Text
Resource Type	Article

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