Research On The Forecast Of E-commerce Sales Volume Based On Linear Mixed Model

Since the 21st century,with the rapid development of technology and the Internet,Internet companies have gained a wide range of development space,and e-commerce platforms have also emerged overnight.The good development of an e-commerce company is inseparable from the good grasp of the trend of its own products.The ancients cloud,"everything is pre-established,not pre-emptive." However,the forecast of sales volume will be affected by many factors,and there are many uncertain factors that will lead to changes in sales.Although the previous time series analysis methods are mature and widely used,each method has its own limitations,such as the fixed order problem of ARIMA model,the complexity of neural network model,and the support vector machine kernel function.Choosing problems,etc.,often leads to the prediction that the effect is not as high as people expected.This paper proposes a modeling method that uses the mixed linear model(LMM)to predict sales volume,combines the relevant knowledge of feature engineering,performs feature construction and feature selection,and further selects the fixed effect of the model based on the selected features.Random effects,different from traditional time series analysis models,by choosing random effects to reflect the seasonality,periodicity,and other uncertainties of time series.The empirical analysis of the sales volume data of Jingdong Mall in the past four years is carried out,and the prediction effects of neural network and support vector machine model are compared and analyzed,and the effectiveness of the method is illustrated.To help the decision-making level of the enterprise to carry out the layout of production resource planning and market development and development,further deepen the development of the enterprise and enhance the competitiveness of the enterprise.