The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Change-point detection in time-series data by relative density-ratio estimation
Open accessAbstract
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 samples from 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 artificial and real-world datasets including human-activity sensing, speech, and Twitter messages, we demonstrate the usefulness of the proposed method.
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Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016) · arXiv · doi:10.48550/arxiv.1606.06565 · Cited by 1,314
Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding
Deldari, S., Smith, D. V., Xue, H., & Salim, F. D. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449903 · Cited by 136
“The methods in RuLSIF [30], KLIEP [54] and SEP [3] used a non-parametric Gaussian kernel to model the density ratio distribution between subsequent time intervals.”
InfluentialReal-Time Change Point Detection with Application to Smart Home Time Series Data
Aminikhanghahi, S., Wang, T., & Cook, D. J. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2850347 · Cited by 123
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for Processing Heterogeneous Sensor Data
Deldari, S., Smith, D. V., Sadri, A., & Salim, F. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3411832 · Cited by 48
Bayram, F., Ahmed, B. S., & Hallin, E. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112184 · Cited by 16
Metadata
- Authors
- Liu, S., Yamada, M., Collier, N., & Sugiyama, M.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2013-07
- Volume
- 43
- Issue
- 275
- Pages
- 72-83
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W1682770403
- Cited by
- 447
- References
- 50
- FWCI
- 29.04
- Type
- journal article
- Language
- en
- Open access
- green
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