Research on Extreme Weather Risk Forecasting Model Based on ARIMA and EWM-TOPSIS Algorithms

This study presents a quantitative risk forecasting model based on ARIMA and EWM-TOPSIS algorithms, designed to assess and predict the impact of extreme weather events on insurance underwriting decisions. The ARIMA model, with its precision in time series forecasting, effectively captures and predicts the future frequency of occurrence of extreme weather events. Meanwhile, the EWM-TOPSIS algorithm comprehensively evaluates underwriting risks by assigning weights and integrating evaluations, taking into account multiple key indicators. The integration of ARIMA's predictive capabilities with the evaluative strength of the EWM-TOPSIS method offers several advantages. It enhances the model's predictive accuracy by utilizing historical meteorological data while ensuring a robust assessment through a balanced consideration of multiple quantitative factors. The model's robustness is further validated through sensitivity analysis, which tests the stability of the model under various parameter variations, ensuring the reliability of the outcomes.