23 citations as recorded by crossref.

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4. A shifting level model algorithm that identifies aberrations in array-CGH data A. Magi et al. https://doi.org/10.1093/biostatistics/kxp051
5. Dynamic programming approach for segmentation of multivariate time series H. Guo et al. https://doi.org/10.1007/s00477-014-0897-0
6. Separation of the impacts of climate change and human activity on runoff variations C. Zhao et al. https://doi.org/10.1080/02626667.2013.865029
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9. MoCom: Motion-Based Inter-MAV Visual Communication Using Event Vision and Spiking Neural Networks N. Zhang et al. https://doi.org/10.1109/TRO.2026.3677077
10. A novel segmentation approach for work mode boundary detection in MFR pulse sequence K. Chi et al. https://doi.org/10.1016/j.dsp.2022.103462
11. Modified dynamic programming approach for offline segmentation of long hydrometeorological time series A. Gedikli et al. https://doi.org/10.1007/s00477-009-0335-x
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14. A Novel Approach for the Detection of Inhomogeneities Affecting Climate Time Series A. Toreti et al. https://doi.org/10.1175/JAMC-D-10-05033.1
15. Dynamic programming-based optimization for segmentation and clustering of hydrometeorological time series H. Guo & X. Liu https://doi.org/10.1007/s00477-015-1192-4
16. Segmentation algorithm for long time series analysis A. Gedikli et al. https://doi.org/10.1007/s00477-007-0115-4
17. A hybrid segmentation method for multivariate time series based on the dynamic factor model Z. Sun et al. https://doi.org/10.1007/s00477-016-1323-6
18. Automatic segmentation of long-term ECG signals corrupted with broadband noise based on sample entropy P. Micó et al. https://doi.org/10.1016/j.cmpb.2009.08.010
19. Quasi-differentiation and its applications to noisy time series data from complex systems S. Cheong et al. https://doi.org/10.1038/s41598-025-26106-w
20. Fast segmentation algorithms for long hydrometeorological time series H. Aksoy et al. https://doi.org/10.1002/hyp.7064
21. Letter to the editor H. Aksoy et al. https://doi.org/10.1007/s00477-006-0091-0
22. Adaptive G–G clustering for fuzzy segmentation of multivariate time series L. Wang et al. https://doi.org/10.1007/s00477-020-01817-w
23. Hybrid dynamic learning mechanism for multivariate time series segmentation L. Wang et al. https://doi.org/10.1002/sam.11448