对搜索量类别和实际购买的时间感知预测
Shahed Abdullhadi1, Dana A Al-Qudah1, Bilal Abu-Salih1
1King Abdullah II School of Information Technology, The University of Jordan, Jordan.
Heliyon
|February 6, 2024
概括
本研究使用数据挖掘来预测电子商务产品趋势和最佳销售时间. 线性回归在使用多变量时间序列数据预测需求方面表现最好.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 电子商务分析 电子商务分析
背景情况:
- 越来越多的电子商务环境需要预测产品趋势和最佳销售时间的工具.
- 企业需要准确的需求预测,以保持竞争力和有效地管理库存.
研究的目的:
- 开发和评估数据挖掘算法,用于预测电子商务中的产品需求.
- 用多变量时间序列数据确定最有效的模型来预测产品趋势.
主要方法:
- 通过API收集实时数据,并补充了来自Keywords Everywhere和Helium 10的搜索量数据.
- 多变量时间序列数据经过严格的处理和验证.
- 实施和比较了XGBoost,线性回归,随机森林,长期短期记忆 (LSTM) 和K-近邻 (KNN) 模型.
主要成果:
- 线性回归表现出优异的性能,确定系数 (R2) 为90.688%,根平均平方误差 (RMSE) 为0.057.
- XGBoost 取得了强的结果,R2 = 85.89% 和 RMSE = 0.062.062.
- 此外,KNN和Random Forest也显示出具有竞争力的预测能力,其R2值分别为85.129%和84.854%.
结论:
- 线性回归对于电子商务需求预测非常有效,提供最好的预测准确度.
- 该研究验证了各种机器学习模型在分析电子商务应用程序的时间序列数据中的实用性.
- 通过数据挖掘准确的需求预测可以显著帮助企业在战略决策和库存管理.
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