Extension Classified Prediction Used in Predicting Monthly Average Temperature of Cities

Abstract

This paper presents a new method of prediction -- extension classified prediction, it is used to predict monthly average temperature of cities. The historical data of the monthly average temperature of cities and precipitation of cities and sunshine hours of cities are used to establish classified classics field and node field element. The dependent function of material element and extension set are applied to establish prediction model. The prediction results can be obtained by means of classified analysis. Through analyzing and calculating the real data of a certain city, the results show that extension classified prediction is effective in predicting monthly average temperature of cities.