Regional Rural Economic Forecasting Based on Single Exponential Smoothing Model with Outlier points

Abstract

The article addresses the problem that the traditional single exponential smoothing model (SES) is disturbed by outliers in the time series and is not easy to find, and searches for outliers and removes them with the help of subsets, to construct a single exponential smoothing model (SESO) based on outliers, and takes Guangxi Fangchenggang as an example to forecast the primary industry and rural residents’ disposable income The results show that the SESO model has high prediction accuracy and credible results The SESO model can make predictions of regional rural economy-related indicators well; corresponding policies are proposed for the characteristics of rural economic changes in Fangchenggang, Guangxi.