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.
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“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
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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
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