Abstract
In this paper, GBDT machine learning is introduced into the demand forecasting of catering e-commerce, and a weather-sensitive demand forecasting model based on GBDT is constructed. In addition, twenty-two influence factors are selected in the model, including minimum daily temperature, average temperature, highest daily temperature, minimum daily pressure, average pressure, highest daily pressure, 20-20 hours precipitation, hours of sunshine, extreme wind speed, direction of extreme wind speed, average wind speed, average aqueous vapor pressure, average relative humidity, maximum wind speed, direction of maximum wind speed, minimum relative humidity, logarithm of sales volume of last week, average difference in temperature, average difference in precipitation, promotion factor and holiday factor. Then, the model is applied to an empirical study to analyze the data and forecast the future demand. This paper also compares the results with several models including linear regression model, passenger flow model and BP neural network model. Finally, the experimental results show that the demand forecasting model based on GBDT has the highest accuracy and is an effective solution to solve the high demand fluctuation of catering e-commerce business.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: DEStech Transactions on Engineering and Technology Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.