Forecasting Method And Research Based On The Insurance Time Series Data

After learning the knowledge related to time series data,the article has selected relevant issues to study,so the main content of this article is the analysis and prediction of domestic insurance income data.Taking property insurance and life insurance as examples,the corresponding data analysis methods and model selections are given.At first,the article introduces the relevant research background and theoretical knowledge,which is helpful for the subsequent case analysis and future learning and research.This article mainly uses the ARIMA model,the deterministic factor decomposition model,the product seasonal model,the residual autoregressive model,and a combination of multiple models to analyze the data,and gives multiple fitted prediction models for property insurance and life insurance,respectively.And select the best model for prediction,and give the prediction data.Comprehensive analysis found that all insurances have growth trends and seasonality,and the optimal model is a comprehensive model,but the models within the comprehensive model are different,and then we can get the analysis and prediction of the domestic insurance income data.The optimal model is the same.However,due to the different internal models of different insurances,it is necessary to analyze one by one to get the best results.But in general,China's insurance development trend is better,showing an upward trend,which can better promote China's economic development.