Abstract
Climate risk poses significant threats to human life and property. Climate insurance can effectively mitigate and disperse these risks. This paper addresses the weakness in weather risk prediction for climate insurance formulation by combining Ensemble Empirical Mode Decomposition (EEMD) and Autoregressive Integrated Moving Average (ARIMA) models. Four models, namely EMD, EEMD, ARIMA, and EMD-ARIMA, were established for modeling and forecasting Chinas annual precipitation data. The results show that the EEMD-ARIMA model can suppress the modal aliasing problem in time series and has the best fit compared to other models. This model can more accurately describe the variation in annual precipitation in forecasting applications, providing significant predictive value for insurance companies and government decisions regarding insurance and climate risk management.
Published Version
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