Research And Application Of Combination Forecast Model Of Grain Production Based On Python

As the foundation of human survival,food plays a decisive role in the stability of a country.As a large grain-producing country,China has always placed the development of food production at the forefront.With the development of modern technology,people are constantly trying to apply current technology and theory to food production technology,and forecasting technology is one of them.As a mature technology,predictive technology has brought a lot of convenience to people.From the change of weather conditions to the prediction of space trajectory,it has exerted tremendous influence on the development of human life,and the application of forecasting technology in food production.It can effectively improve and prevent a series of problems in the food production process.The use of forecasting technology can make a certain assessment of future grain output,so that grain production in some regions and even countries can be planned in advance to increase production and income,without wasting food.There are many ways to predict food production.Different regions predict the accuracy of different prediction methods for different regions,environments,and crops.Therefore,it is impossible to predict everything in a single way.Since it is necessary to collect a large amount of data and process it to accurately predict food production,it takes a long time to implement a prediction model.With the establishment of various big data platforms,collecting data is no longer as complicated as it used to be.However,because of the increasing amount of data,people need software that can have both collection and processing functions to complete tasks,so Python appeared to People's vision.Python has powerful data processing capabilities and a wealth of libraries that make Python the task of most data processing.At the same time,because Python is a software with crawler capabilities,collecting data is also very simple,which allows Python to contribute to food forecasting.It can handle the work of a single predictive model faster,which also provides conditions and possibilities for combined forecasting of grain.This article will use Python programming and call library functions,such as Math,Numpy,scikit-learn,and Pandas,for four common food prediction methods: exponential smoothing,gray GM(1,1),regression analysis,and support vector machine.Modeling work separately,compiling it into a Python program and testing its accuracy.Then the program assigns weights to different models according to different weights,and selects a more accurate method to filter multiple A single predictive model is combined into a new predictive model which can test to obtain a combined forecasting model that can complement each other,be more stable,and have a wider range of applications to achieve more accurate predictions of grain yield.