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A Study of Disaster Insurance in Extreme Weather Based on Logistic Regression and ARIMA Models

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DOI: 10.23977/acss.2024.080412 | Downloads: 0 | Views: 32

Author(s)

Shuting Wang 1

Affiliation(s)

1 Dept of Math, Qilu Normal University, Jinan, China

Corresponding Author

Shuting Wang

ABSTRACT

This paper examines the impact of extreme weather events on insurance under-writing decisions and real estate development. Using a combination of statistical mod-els, including Topsis entropy weight method and Logistic regression, we analyze the correlation between extreme weather indicators and insurance claims. Building upon this analysis, we investigate the factors influencing housing sales rates and forecast disaster losses using the ARIMA model. By treating the housing sales rate as a proxy for insurance compensation rates, we refine the insurance claims and profit model, providing insights for insurance underwriting decisions in different regions. Our findings offer new perspectives on mitigating risks and optimizing insurance policies in the face of changing environmental and social factors.

KEYWORDS

Topsis, Extreme weather, Logistic model, ARIMA

CITE THIS PAPER

Shuting Wang, A Study of Disaster Insurance in Extreme Weather Based on Logistic Regression and ARIMA Models. Advances in Computer, Signals and Systems (2024) Vol. 8: 83-90. DOI: http://dx.doi.org/10.23977/acss.2024.080412.

REFERENCES

[1] LI Yanbo, LIU Miaoyang, YANG Kai, et al. Optimal scheduling of mobile power vehicles with self-consistent energy system for highways under extreme weather [J/OL]. Journal of Jilin University (Engineering Edition), 1-10[2024-05-25]. https://doi.org/10.13229/j.cnki.jdxbgxb.20240224.
[2] Luo Qian, Li Yongmei, Wang Tenghua, et al. Constructing an evaluation system of rational medication use indexes in clinical departments based on improved entropy weight method combined with TOPSIS method [J]. Clinical rational drug use, 2024, 17(15): 170-172+ 177. DOI: 10. 15887/ j. cnki.13-1389/r.2024.15.049.
[3] Dai Daocheng,Yu Chenyang,Song Jihao,et al. Analysis of smartphone user monitoring data based on logistic regression [J]. Modern Information Technology, 2024, 8(08): 36-39. DOI: 10. 19850/ j. cnki. 2096-4706.2024.08.009.
[4] Tian Gengwen. Catastrophe insurance supports "umbrella" in response to disasters [N]. Rural Financial Times, 2024-05-06(A01).
[5] Xiang Junkun, Yu Jiaxing, Gao He, et al. Constructing SWECPX model based on ARIMA to solve the e-commerce demand forecasting problem [J]. China Business Journal, 2024, (08): 29-32. DOI: 10.19699/j.cnki.issn2096-0298.2024.08.029.
[6] WANG Weiqiang, LI Yongkang, SHENG Yali, et al. GF-1 PMS multispectral image reconstruction method based on multiple linear regression model [J]. Journal of Anhui Institute of Science and Technology, 2024, 38(03): 70-77. DOI: 10.19608/j.cnki.1673-8772.2024.0309.

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