通过整合变化模式分解和深度神经网络来预测电子商务产品价格
1Department of Business Administration, Shanxi Polytechnic College, Taiyuan, Shanxi, China.
PeerJ. Computer science
|December 9, 2024
概括
本研究引入了一种新的变化模式分解深度神经网络 (VMD-DNN) 模型,用于准确的电子商务产品价格预测. VMD-DNN模型显著减少了预测错误,并改善了在线零售价格的趋势反射.
科学领域:
- 时间序列分析时间序列分析
- 机器学习 机器学习
- 电子商务分析 电子商务分析
背景情况:
- 电子商务产品价格表现出复杂的非线性和非静止时间序列特征.
- 传统的线性模型难以捕捉这些复杂的价格动态.
- 准确的价格预测对于电子商务运营和战略至关重要.
研究的目的:
- 开发电子商务产品价格的先进分解和预测模型.
- 提高在线零售环境中价格预测的准确性和可靠性.
- 解决处理复杂时间序列数据的传统方法的局限性.
主要方法:
- 变量模式分解 (VMD) 的应用,将价格时间序列分解为内在模式函数 (IMF).
- 使用最小模糊标准来确定VMD的最佳模式数量 (K).
- 使用深度神经网络 (DNN) 预测分解的IMF,然后对最终价格预测进行聚合.
主要成果:
- 拟议的变化模式分解深度神经网络 (VMD-DNN) 模型在公开数据集上实现了低的0.6578%和0.5414%的平均绝对百分比错误 (MAPE).
- 与基线方法相比,证明了66.5%和70.4%的显著错误降低率.
- 获得了高的方向对称性 (DS) 评分86.25%,表明优越的趋势预测准确度.
结论:
- VMD-DNN模型有效地捕捉了电子商务产品价格的非线性和非静止模式.
- 该方法提供了一个可靠的价格预测方法,优于现有的技术.
- 该模型能够准确预测价格趋势的能力提高了其在电子商务分析中的适用性.
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