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PreprintCC BY 4.0arXiv

MSR-CP: A Modified Segmented Regression Approach to Change Point Estimation in Rainfall Patterns in West Bengal

  • Anirban Ghosh
  • Raju Maiti
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Abstract

This study presents a methodology for detecting change point in precipitation series using a smooth version of segmented regression model. We examine several existing change point detection methods and identify their limitations in capturing gradual changes commonly observed in climate data. To address these limitations, we discuss an extension of the traditional segmented regression model enabling the detection of gradual shifts in trend. The developed methodology is further extended to accommodate dependent error structures, which are common in time series data. Extensive simulation studies are conducted to evaluate the performance and computational efficiency of the proposed approach relative to existing methods. The methodology is subsequently applied to 60 years of monsoon rainfall data from two districts of West Bengal, India. This approach offers improved insights into evolving rainfall patterns and serves as a useful tool for climate data analysis and long-term forecasting.

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Cite

BibTeXRIS
BibTeX
@misc{ghosh2026msrcp,
  title = {{MSR-CP: A Modified Segmented Regression Approach to Change Point Estimation in Rainfall Patterns in West Bengal}},
  author = {Anirban Ghosh and Raju Maiti},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.07858},
  eprint = {2611.07858},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.07858},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - MSR-CP: A Modified Segmented Regression Approach to Change Point Estimation in Rainfall Patterns in West Bengal
AU  - Anirban Ghosh
AU  - Raju Maiti
PY  - 2026
DA  - 2026/11/05
PB  - arXiv
DO  - 10.48550/arxiv.2611.07858
UR  - https://arxiv.org/abs/2611.07858
AB  - This study presents a methodology for detecting change point in precipitation series using a smooth version of segmented regression model. We examine several existing change point detection methods and identify their limitations in capturing gradual changes commonly observed in climate data. To address these limitations, we discuss an extension of the traditional segmented regression model enabling the detection of gradual shifts in trend. The developed methodology is further extended to accommodate dependent error structures, which are common in time series data. Extensive simulation studies are conducted to evaluate the performance and computational efficiency of the proposed approach relative to existing methods. The methodology is subsequently applied to 60 years of monsoon rainfall data from two districts of West Bengal, India. This approach offers improved insights into evolving rainfall patterns and serves as a useful tool for climate data analysis and long-term forecasting.
ER  -