Gasoline Station Sales Prediction Method Based on Deep Learning and Its Application of Promotion Strategy

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Gasoline Station Sales Prediction Method Based on Deep Learning and Its Application of Promotion Strategy

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[1]LU Chenhui,FENG Shuo,YI Aihua,et al.Gasoline Station Sales Prediction Method Based on Deep Learning and Its Application of Promotion Strategy[J].Journal of Zhengzhou University (Engineering Science),2022,43(01):1-6.[doi:10.13705/j.issn.1671-6833.2022.01.014]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 01 Page number: 1-6 Column: Public date: 2022-01-09

Title:: Gasoline Station Sales Prediction Method Based on Deep Learning and Its Application of Promotion Strategy

Author(s):: LU Chenhui¹; FENG Shuo¹; YI Aihua²; YE Xiaojun¹; 1.School of Software, Tsinghua University, Beijing 100084, China;
2.Sinopec Sales Co., Ltd.，Guangdong Branch, Guangzhou 510620, China

Keywords:: sales prediction; data-driven decision; deep learning; recurrent neural network; artificial intelligence applications

CLC:: TP181

DOI:: 10.13705/j.issn.1671-6833.2022.01.014

Abstract:: Promotion strategy is an important part of gas station business, and data-driven promotion strategy has become an urgent demand for gas stations to achieve precise marketing. A deep learning model was proposed for forecasting gasoline sales based on historical gas station data, promotion strategies and key features, and a promotion strategy formulation method based on sales forecasting models. Due to the historical data characteristics of gas stations, a multi-level network structure was designed to process data of different types, and combine promotion strategy information to perform oil sales forecasts. In addition, by introducing key features, the accuracy of the sales forecast model was improved; by inputting different promotion strategies, the automatic selection of gas station marketing strategies was realized. The results of experiments conducted on a data set constructed from real gas station data showed that the sales forecast model proposed had lower forecast errors than other mainstream methods

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Last Update: 2022-01-09