Research On Maize Price Prediction Method Based On Improved Temporal Convolutional Network

Commodity prices are of great significance to the healthy development of market economy and the rational allocation of resources.Changes in the price of agricultural products are directly related to the normal operation of a country’s economy,and the price of agricultural products is an important determinant.Taking China’s agricultural production as the background,this paper theoretically discusses the regulating effect of agricultural production factors on the price of agricultural production factors through the analysis of agricultural production factors in the process of agricultural production,and provides data support for the development and improvement of market price research of agricultural production factors.In recent years,time convolutional networks(TCN)have been widely used in corn market forecasting as an emerging deep learning method.This paper takes time convolutional network(TCN)as the core idea,adopts modern time series analysis and forecasting method,uses long short-term memory network(LSTM)idea to optimize TCN,and conducts empirical analysis based on practical problems.In summary,the main findings of this paper are as follows:1.Using the characteristics of spatial convolutional structure and weight distribution,a new exploration of corn market is carried out.Through the learning of historical data,the main factors affecting the price change of maize are automatically extracted,and based on these factors,future price predictions are made.2.Through the time convolutional network,the use of historical data to analyze and predict future price changes,so as to provide a reliable basis for corn market forecasting,this paper takes corn price as the starting point to analyze the corn price fluctuation trend.And the introduction of TCN and long-term memory network(LSTM)enhances the storage memory based on the gate structure,relying on the control advantage.3.In this paper,an improved temporal convolutional network model is proposed,which improves the prediction accuracy of the TCN prediction model combined with the gate structure,and improves the speed of computation by combining the mode of convolution calculation.By comparing the single model TCN,LSTM and the optimized combination model,it is shown that the improved temporal convolutional network model has higher accuracy and greater applicability in the whole index.