NDLI: Distance measures for effective clustering of ARIMA time-series

Content Provider	IEEE Xplore Digital Library
Author	Kalpakis, K. Gada, D. Puttagunta, V.
Copyright Year	2001
Description	Author affiliation: Dept. of Comput. Sci. & Electron. Eng., Maryland Univ., Baltimore, MD, USA (Kalpakis, K.)
Abstract	Much environmental and socioeconomic time-series data can be adequately modeled using autoregressive integrated moving average (ARIMA) models. We call such time series "ARIMA time series". We propose the use of the linear predictive coding (LPC) cepstrum for clustering ARIMA time series, by using the Euclidean distance between the LPC cepstra of two time series as their dissimilarity measure. We demonstrate that LPC cepstral coefficients have the desired features for accurate clustering and efficient indexing of ARIMA time series. For example, just a few LPC cepstral coefficients are sufficient in order to discriminate between time series that are modeled by different ARIMA models. In fact, this approach requires fewer coefficients than traditional approaches, such as DFT (discrete Fourier transform) and DWT (discrete wavelet transform). The proposed distance measure can be used for measuring the similarity between different ARIMA models as well. We cluster ARIMA time series using the "partition around medoids" method with various similarity measures. We present experimental results demonstrating that, using the proposed measure, we achieve significantly better clusterings of ARIMA time series data as compared to clusterings obtained by using other traditional similarity measures, such as DFT, DWT, PCA (principal component analysis), etc. Experiments were performed both on simulated and real data.
Sponsorship	IEEE Comput. Soc. Tech. Committe on Pattern Anal. & Machine Intelligence (TCPAMI)
Starting Page	273
Ending Page	280
File Size	925684
Page Count	8
File Format	PDF
ISBN	0769511198
DOI	10.1109/ICDM.2001.989529
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-11-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear predictive coding Time measurement Discrete Fourier transforms Discrete wavelet transforms Cepstral analysis Principal component analysis Cepstrum Euclidean distance Indexing Fourier transforms
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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