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Curve Fitting by Segmented Straight Lines
Abstract
In many situations approximation of a set of data by a polygonal curve is more advantageous than approximation by a polynomial. If the join points of the polygonal curve are known, the problem is quite simple. If, however, they are to be chosen in some expeditious fashion, considerable numerical difficulties can arise if the curve-fitting problem is approached directly. In this paper it is shown that dynamic programming offers a simple direct approach to the determination of an optimal fit.
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Citations
Cited by
Computation and analysis of multiple structural change models
Bai, J., & Perron, P. (2003) · Journal of Applied Econometrics · doi:10.1002/jae.659 · Cited by 5,771
Algorithms for the optimal identification of segment neighborhoods
Auger, I. E., & Lawrence, C. E. (1989) · Bulletin of Mathematical Biology · doi:10.1007/bf02458835 · Cited by 326
Segmentation of biological multivariate time-series data
Omranian, N., Mueller-Roeber, B., & Nikoloski, Z. (2015) · Scientific Reports · doi:10.1038/srep08937 · Cited by 28
Metadata
- Authors
- Bellman, R., & Roth, R.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1969-09
- Volume
- 64
- Issue
- 327
- Pages
- 1079-1084
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1986534397
- Cited by
- 170
- References
- 0
- FWCI
- 2.72
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Curve Fitting by Segmented Straight Lines. https://app.cochat.ai/papers/10.1080/01621459.1969.10501038
- The paper
- Bellman, R., & Roth, R. (1969). Curve Fitting by Segmented Straight Lines. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1969.10501038
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.
