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A comparative online sales forecasting analysis: Data mining techniques
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DOI：10.1016/j.cie.2022.108935.png)
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摘要
En 中文
This study aims to improve the management efficiency of e-commerce platform and assists merchants on the e -commerce platforms in formulating a suitable sales plan urgently. Online sales forecasting analysis needs to be studied and shows that the management efficiency and operating income on an e-commerce platform is improved through accurate commodity sales forecasting. A novel online clothing sales forecasting model is proposed based on data mining technique. This study contributes to presenting the model references for e-commerce platform to make decisions on future sales and directions. (1) The gray correlation model was employed to mine the cor-relation degree between each feature and the clothing sales to select the features that have a great impact on clothing sales. (2) A sailfish optimization algorithm (SFO) algorithm with random disturbance strategy (SFOR) was proposed based on the SFO to improve the prediction effect of clothing sales. The benchmark function test results showed that the SFOR algorithm effectively avoided local extreme points. (3) The SFOR algorithm was used to solve the extreme learning machine (ELM) random parameter problem, and the SFOR-ELM-based online sales prediction model of clothing products suitable for multiple scenarios was constructed. In addition, three cases are applied to verify the SFOR-ELM-based online clothing sales forecast model. The verification results proved that SFOR-ELM achieved satisfactory prediction results, with its mean absolute percentage error values controlled below 5.1% and root mean square error values controlled below 16.2%.
Keyword:
Data mining
Online clothing sales
Sales forecasting
Optimization algorithm
Extreme learning machine
期刊
IF:
6.5
论文数:
1.0W
被引数:
3.8W
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