Agricultural Product Price Prediction And System Realization Based On Improved Temporal Convolutional Network

The stable operation of agricultural products market not only affects the national economic development,but also one of the guarantees for the people to live and work in peace and contentment.The prediction of the price trend of agricultural products often affects the production planning of agricultural workers and the consumption habits of consumers.However,at present,there is no mature platform for the prediction of the price trend of agricultural products in the market.The majority of agricultural workers and consumers can not obtain the prediction information of the future trend of the price of agricultural products in a timely and convenient manner.Aiming at this pain spot,we use deep learning technology to develop a price forecasting system applied to the field of agricultural products,which is not only consistent with the new mode of Internet plus agriculture,but also helps the country to promote the policy of precise poverty alleviation.Temporal convolution network(TCN)is a relatively new research achievement in recent years.It is more significant to capture the characteristic information of time series through the way of dilated convolution structure and weight sharing.It is a new attempt to apply TCN to the price prediction of agricultural products.In addition,on the basis of TCN,the gates of long and short-term memory network(LSTM)are introduced to enhance the advantage of long-term dependent memory control of time series.In this paper,an improved temporal convolution network model is proposed.In addition,nine classes of data sets are compared with single model such as multilayer feedforward network(BP),support vector regression(SVR),TCN,LSTM and combined model BP-ARIMA.The experiments show that the improved temporal convolution network model has higher accuracy in overall index.Around the core of agricultural product price prediction module,this paper adopts the development mode of front-end and back-end separation,and applies the architecture idea of a large front-end and multiple back-end services to realize the PC and mobile system which can provide agricultural product prediction and auxiliary functions.Finally,the system is completed the pressure test,and the response time and other indicators reflect that compared with the past single architecture it should be better in scalability,stability and concurrency,which will have practical application value in the future,whether it extend itself or combined with other platforms.