Change-point detection in time-series data by relative density-ratio estimation
Pages 363 - 372
Abstract
The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm that is based on non-parametric divergence estimation between two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on real-world human-activity sensing, speech, and Twitter datasets, we demonstrate the usefulness of the proposed method.
References
[1]
Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes: Theory and Application. Prentice-Hall, Inc., Upper Saddle River (1993).
[2]
Gustafsson, F.: The marginalized likelihood ratio test for detecting abrupt changes. IEEE Transactions on Automatic Control 41(1), 66-78 (1996).
[3]
Takeuchi, Y., Yamanishi, K.: A unifying framework for detecting outliers and change points from non-stationary time series data. IEEE Transactions on Knowledge and Data Engineering 18(4), 482-489 (2006).
[4]
Moskvina, V., Zhigljavsky, A.: Change-point detection algorithm based on the singular-spectrum analysis. Communications in Statistics: Simulation and Computation 32, 319-352 (2003).
[5]
Kawahara, Y., Yairi, T., Machida, K.: Change-point detection in time-series data based on subspace identification. In: Proceedings of the 7th IEEE International Conference on Data Mining, pp. 559-564 (2007).
[6]
Kawahara, Y., Sugiyama, M.: Sequential change-point detection based on direct density-ratio estimation. Statistical Analysis and Data Mining 5(2), 114-127 (2012).
[7]
Sugiyama, M., Suzuki, T., Kanamori, T.: Density Ratio Estimation in Machine Learning. Cambridge University Press, Cambridge (2012).
[8]
Sugiyama, M., Suzuki, T., Nakajima, S., Kashima, H., von Buenau, P., Kawanabe, M.: Direct importance estimation for covariate shift adaptation. Annals of the Institute of Statistical Mathematics 60(4), 699-746 (2008).
[9]
Desobry, F., Davy, M., Doncarli, C.: An online kernel change detection algorithm. IEEE Transactions on Signal Processing 53(8), 2961-2974 (2005).
[10]
Kanamori, T., Hido, S., Sugiyama, M.: A least-squares approach to direct importance estimation. Journal of Machine Learning Research 10, 1391-1445 (2009).
[11]
Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., Sugiyama, M.: Relative density-ratio estimation for robust distribution comparison. Advances in Neural Information Processing Systems 24, 594-602 (2011).
[12]
Ali, S.M., Silvey, S.D.: A general class of coefficients of divergence of one distribution from another. Journal of the Royal Statistical Society, Series B 28(1), 131-142 (1966).
[13]
Csiszár, I.: Information-type measures of difference of probability distributions and indirect observation. Studia Scientiarum Mathematicarum Hungarica 2, 229-318 (1967).
[14]
Liu, S., Yamada, M., Collier, N., Sugiyama, M.: Change-point detection in timeseries data by relative density-ratio estimation. arXiv 1203.0453 (2012).
- Change-point detection in time-series data by relative density-ratio estimation
Recommendations
Change-point detection in time-series data by relative density-ratio estimation
The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series ...
Sequential change-point detection based on direct density-ratio estimation
Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this ...
Multi-stage change-point detection scheme for large-scale simultaneous events
Change-point detection schemes, which represent one type of anomaly detection schemes, are a promising approach for detecting network anomalies, such as attacks and epidemics by unknown viruses and worms. These events are detected as change-points. ...
Comments
Information & Contributors
Information
Published In
November 2012
754 pages
ISBN:9783642341656
Sponsors
- Hokkaido University
- IAPR: International Association for Pattern Recognition
- Chiba University: Chiba University, Japan
- Tohoku University: Tohoku University
- HU: Hiroshima University
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 07 November 2012
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 25 Jan 2025