The Short-term Prediction Of Chinese Vegetable Price Based On Chaotic Neural Network Model

Vegetable is one of the important consumer goods for residents’ daily life, and is also an importantindustry in supporting rural economic development. The fluctuation of vegetable price has an impact onfarmers’ income and the residents’ life. Besides, it’s an important responsibility of government work tokeep the vegetable price stability. In recent years, the phenomenon that vegetable prices up and downappears frequently in our country. The event that vegetable price abnormal fluctuates including “SuanNi Hen, Dou Ni Wan and Jiang Ni Jun et.al” resulted a series of negative effects for farmers’ incomes,residents’ life, market order and social stability. How can we accurately predict the operating conditionsof vegetable market in the future and then ensure the smooth running of vegetable price, safeguard thestability of farmers’ income and the consumers’ life. This question is gradually becoming the focus ofattention from all walks of life, and one of the focuses in the research study area.So this study want to achieve accurate prediction for vegetable market price in the futuredevelopment trend and the purpose of stable vegetable market running smoothly by putting the researchof short-term prediction of vegetable market price as the breakthrough point. Based on full access torelated literature at home and abroad, this paper mainly carried out the research from the following theaspects according to research shortcoming:Firstly, comprehensive combed the theory and method about vegetable price short-term predictionfrom domestic and foreign, including linear prediction method and neural network method which as therepresentative of non-linear prediction method. At the same time, this paper analyzed the applicablescope and the advantages and disadvantages of each method;Secondly, the fluctuated rules and characteristics of vegetable price were studied systematically.On this basis, we analyzed the vegetable price fluctuations factors from its production and consumption;Thirdly, building the chaotic neural network model by application intelligent information analysistechniques and the model was used to perform a time-series modeling and forecasting analysis for thedaily prices of cabbage. And then we compare the predicted results of the model with an econometricforecasting model-ARIMA.Main results in this study can be summarized as follows:(1)The price fluctuation of vegetables shows an increasing tendency in recent years. Among eachfluctuation components, the seasonal fluctuations has contribution rate of56.16%to the whole pricefluctuation, which is the largest contribution;(2)The chaotic neural network model is much better than ARIMA model in the research ofshort-term prediction of vegetable price in term of prediction accuracy and fitting effect;(3) Empirical analysis shows that as the representative of an artificial intelligence model, chaoticneural network has broad application prospects in short-term monitoring and early warning of vegetableprices.In this study, we achieve certain result on the application of chaotic neural network and short-term prediction of vegetable price. For the application of artificial intelligence methods in the short-termmonitoring and early warning of agricultural prices, though this research did some exploring and trying,there is also some deficiency. We will continue to focus on the application of artificial intelligencemethod in short-term monitoring, early warning and control optimization of vegetable prices, so as toprovide technical and policy support in stabilizing prices of agricultural products.