Prediction Of Corn Market Price Based On Data Mining

The price fluctuations of agricultural products and its trend are closely related to people's lives.The corn is one of the major grain production in China,and the annual production is very large,and the planting is also very wide.What is more,the consumption of the corn for food crops,feed crops,fuel crops and other aspects is also great.The annual production and consumption of corn are large,and they are closely related to the income of farmers,so in recent years,the grain yield,the price and demand of corn are concerned.What is more,the price is one of the most concerned aspect.Over the years,many domestic and foreign scholars have done a lot of researches,and many ways of the time series model are applied to various price analysis and forecast.in the end,the effect is better than the linear model.Today,the method of data mining is popular.There are many reasons.On the hand,the ways of various algorithms has high accuracy and it has good effect,but also can be applied to various fields,on the other hands,they are not limited by many conditions.Also,the computing speed of various methods is quite fast.In recent years,many scholars have applied data mining algorithms to the analysis of agricultural products.In general,we can use the way of the combination of statistics and data mining algorithms solve practical problems quickly and well.I aims to solve practical problems in this paper,and I choose the price of agricultural products-corn,which will be researched.On the basis of ways of all the scholars,I consider and choose the appropriate method.And I will select the optimal model for the final prediction according to the actual problem.By using the R tools,I establish the time series model,the neural network model to solve the problems.For time series model,the AR(1),ARIMA(1,1,1)ARIMA(2,1,3),seasonal model and seasonal model ARIMA(2,1,3)and seasonal model ARMA(2,2)of the 5 models,are selected.For the neural network model,I choose the first five months,six months and twelve months respectively as input neurons,and choose future data of one month,two months,three months,four months and five month as output neurons.And then,I set the number of hidden units,back propagation rate and the number of iterations of thelayer index.In total,I set about 45 models,and then choose good models.In the end,I select the optimal model by comparing the two methods.Through the analysis of the specific problem,we can get the conclusion that:(1)There are many factors that influence the price fluctuation of agricultural products.It is very difficult to use the linear model to combine many factors.But the neural network can solve the nonlinear problem.Neural network can approximate any nonlinear function with any precision.In this paper,one of the modeling methods which I choose is neural network modeling.(2)Through the analysis of the influencing factors,we can see that there are a lot of indicators can not be quantified,so I decide predict price from the perspective of price trends.(3)Through the comparison of the results of various models,we can see that the prediction results are relatively small,thorough the neural network algorithm in the market price forecast.Finally,the neural network model is chosen to predict the price.And we obtained the prediction results.