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dynamically integrated regression model for online auction data

Science China Mathematics 2022年第7期65卷 1531-1552页

作者： Mengying You Huazhen Lin Hua Liang Center of Statistical Research School of StatisticsSouthwestern University of Finance and EconomicsChengdu 611130China Department of Statistics George Washington UniversityWashingtonDC 20052USA

We propose a dynamically integrated regression model to predict the price of online auctions,including the final price.Different from existing models,the proposed method uses not only the historical price but also the information from bidding time.Consequently,the prediction accuracy is improved compared with the existing methods.An estimation method based on B-spline approximation is proposed for the estimation and the inference of parameters and nonparametric functions in this model.The minimax rate of convergence for the prediction risk and large-sample results including the consistency and the asymptotic normality are established.Simulation studies verify the finite sample performance and the appealing prediction accuracy and robustness.Finally,when we apply our method to a 7-day auction of iPhone 6s during December 2015 and March 2016,the proposed method predicts the ending price with a much smaller error than the existing models.

关键词： B-spline dynamic forecasting model functional linear regression model minimax rate online auction

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Research on Demand forecasting of Retail Supply Chain Emergency Logistics Based on NRS-GA-SVM

Research on Demand Forecasting of Retail Supply Chain Emerge...

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第30届中国控制与决策会议

作者： Hong Xue Chi Jiang Boyu Cai Yi Yuan School of Computer and Information Engineering Beijing Technology and Business University

Aiming at the characteristics of high dimension, dynamic condition and parameter self-adaptation for demand forecasting, the demand forecasting model of retail supply chain emergency logistics was created based on NRS-GASVM algorithm. The sample attribute index reduction model for emergency logistics demand forecasting was established based on NRS algorithm. The continuous data processing method was adopted. The key influencing factors were extracted more accurately. The dynamic demand forecasting model of emergency logistics was established based on nonlinear support vector machine regression theory and parameter optimization machine learning algorithm in order to get the optimal prediction effect. The numerical experiment results show that preprocessing the indexes with NRS and optimizing parameters with GA can not only improve the accuracy of the emergency logistics demand forecasting results,can but also reduce the execution time of the forecasting model, which promotes the emergency safeguard capability of retail supply chain emergency and verifies the feasibility of emergency logistics demand forecasting model for retail supply chain.

关键词： Retail Supply Chain Emergency Logistics Demand dynamic forecasting model Machine Learning NRS-GA-SVM Algorithm

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