Prediction Of Commodity Price Based On Graph Network Theory

In recent years,the huge price fluctuations of commodities have damaged China's economy.As the country with the largest import volume of commodities,China has little say in the pricing power of commodities.This case,the combination of market experience,the correct analysis of the influencing factors of commodity prices and its variation rule,using the scientific method is an effective prediction for commodities prices,there is no doubt to our country the government's economic policy formulation,enterprise management strategy choice,family and personal life direction is of great significance.Commodity price prediction has always been a hot topic in various fields,but there are many factors affecting commodity prices,such as government decrees,market cyclical fluctuations,and individual speculation.The traditional price prediction model can hardly meet the need of accuracy under the current price system.Firstly,this paper summarizes the theory of product price transmission on the industrial chain.According to the linear and nonlinear conduction in the process of price conduction,the factors affecting the spot price fluctuation of bulk commodities are summarized.Finally,the paper reviews the development of price prediction theory according to the influencing factors and points out the problems existing in the existing forecasting models.Then it puts forward the prediction of product price on the industrial chain by combining the graph network theory with the neural network.This composite algorithm can not only give full play to the advantages of graph network theory in the representation of industrial network data;At the same time,the neural network algorithm has a good adaptability to the nonlinear characteristics and further improves the effectiveness of the prediction on the superior performance of the graph network theory.The main purpose of this paper is to apply the spot price prediction of bulk commodities based on graphnetwork theory and select the core products of chemical product network for verification.Firstly,the conceptof graph network theory and the internal implementation details are introduced.Secondly,a spot price prediction model based on graph network theory is established to compare the results with other models and realize the simulation prediction of the spot price of commodities.The experimental results show that the key nodes in the network play a vital role in the entire product network.Therefore,enterprises should establish a reasonable and effective early warning mechanism to ensure the production and operation of the key product nodes.While ensuring the integrity of the existing product network,further optimize the industrial chain structure and increase capital investment.On the forecast problem of the commodity prices,more effective price prediction model is proposed in this paper,the industrialchain upstream prediction accuracy is higher,the price of the industry chain upstream and downstream products on the production relationship of structure to fluctuations in the price of the product warning have more positive feedback effect,and have good development prospect and application value.Let the practitioners of the commodity industry,the government intuitively observethe trend of commodity prices,to provide them with certain decision support.