The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A survey of methods for time series change point detection
Open accessAbstract
Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For a relation, Splitting splits time intervals by adopting change-point-detection (CPD) on the time series [Aminikhanghahi and Cook, 2017] that represent the evolution of relation”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Aminikhanghahi and Cook, 2017
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
From time series to complex networks: the visibility graph
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., & Nuño, J. C. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0709247105 · Cited by 1,826
An online algorithm for segmenting time series
Keogh, E., Chu, S., Hart, D., & Pazzani, M. (2002) · Proceedings 2001 IEEE International Conference on Data Mining · doi:10.1109/icdm.2001.989531 · Cited by 1,149
Understanding mobility based on GPS data
Zheng, Y., Li, Q., Chen, Y., Xie, X., & Ma, W.-Y. (2008) · Proceedings of the 10th international conference on Ubiquitous computing · doi:10.1145/1409635.1409677 · Cited by 1,132
A Review and Comparison of Changepoint Detection Techniques for Climate Data
Reeves, J., Chen, J., Wang, X. L., Lund, R., & Lu, Q. Q. (2007) · Journal of Applied Meteorology and Climatology · doi:10.1175/jam2493.1 · Cited by 638 · cited 1×
“Climate analysis, monitoring, and predictionmethods that utilize change point detection have become increasingly important over the last few decades due to the possible occurrence of climate change and the increase in greenhouse gases in the atmosphere [24,32,51].”
Learning transportation mode from raw gps data for geographic applications on the web
Zheng, Y., Liu, L., Wang, L., & Xie, X. (2008) · Proceedings of the 17th international conference on World Wide Web · doi:10.1145/1367497.1367532 · Cited by 616
Cited by
Temporal Knowledge Graph Completion: A Survey
Cai, B., Xiang, Y., Gao, L., Zhang, H., Li, Y., & Li, J. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/734 · Cited by 133
Time Series Data for Equipment Reliability Analysis With Deep Learning
Chen, B., Liu, Y., Zhang, C., & Wang, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3000006 · Cited by 60
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
Large Language Models are Zero-Shot Recognizers for Activities of Daily Living
Civitarese, G., Fiori, M., Choudhary, P., & Bettini, C. (2024) · arXiv · doi:10.48550/arxiv.2407.01238 · Cited by 28
Ma, C., Chitra, U., Zhang, S., & Raphael, B. J. (2022) · Cell Systems · doi:10.1016/j.cels.2022.09.002 · Cited by 27
Metadata
- Authors
- Aminikhanghahi, S., & Cook, D. J.
- Venue
- Knowledge and Information Systems
- Published
- 2016-09-08
- Volume
- 51
- Issue
- 2
- Pages
- 339-367
- ISSN
- 0219-1377 (ISSN-L)
- 0219-3116
- OpenAlex
- W2515822248
- Cited by
- 1,237
- References
- 5
- FWCI
- 46.45
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A survey of methods for time series change point detection. https://app.cochat.ai/papers/10.1007/s10115-016-0987-z
- The paper
- Aminikhanghahi, S., & Cook, D. J. (2017). A survey of methods for time series change point detection. Knowledge and Information Systems. https://doi.org/10.1007/s10115-016-0987-z
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
