Research On Market Demand Forecasting Analysis Method Based On AE-stacking Integrated Learning

In the logistics management,the demand management of the logistics terminal market is an essential link throughout the entire supply chain,and the prediction of the demand of the logistics market is the basis and core content of the supply chain,and is also an important factor affecting the bullock effect.When there is no accurate market demand information,the coordination,control and operation efficiency of each link in logistics management will be significantly affected.However,the current demand forecasting method based on single variable can not meet the requirements of accurate market forecasting.In order to improve the accuracy of the demand forecast of the logistics market,alleviate the bullock effect and optimize the inventory,this paper uses big data to drive the demand decision management of the logistics market,help the enterprises in the logistics management to make the best decision,reduce the operation cost of the whole logistics,and improve the operation efficiency.Therefore,under the background of big data market demand forecasting research,to adapt to the dynamic changes of market demand,and improve the overall efficiency of logistics market and enterprise core competitiveness has important theoretical and practical significance.Combining with the nature of market demand management,this paper takes the content of demand in logistics market as the research object,builds market demand forecasting model,and verifies the validity of the model through comparative experiments.The main research contents are as follows:(1)Different from the traditional single variable time series demand forecasting model,this paper constructs a multi-dimensional characteristic index for comprehensive analysis combined with the relevant factors affecting the supply chain market demand.This paper uses artificial intelligence technology to analyze the influencing weight of multi-dimensional characteristics.The model selects the main characteristic indicators from the three dimensions of commodity information,user information and Logistics Market Information..(2)This paper combines convolution and noise reduction two self coders,constructs the convolution noise reduction self coding neural network algorithm,it is combined with Stacking integrated learning framework.,and proposes an AE-tacking integrated learning demand prediction model method based on multi-dimensional feature index system.Moreover,it can discover the potential information between features better than traditional mathematical models.It can effectively solve the problems of high computational complexity,difficult feature selection and more data noise of high-dimensional data,and adopt the method of ensemble learning fusion,which has better prediction accuracy than the traditional single machine learning algorithm.(3)For the logistics terminal market,data mining technology can be used to achieve accurate forecast of logistics market demand.Combined with the market demand forecast model constructed in this paper,the impact weight of each characteristic index is analyzed,providing a theoretical basis for improving the effectiveness of products to customers.