相关实验视频
使用混合神经网络和影响销售的变量进行零售商店销售预测
Saad Mansur1, Kashif Sattar2, Seyed Ebrahim Hosseini1
1Whitecliffe, Auckland, New Zealand.
PeerJ. Computer science
|September 24, 2025
概括
本研究介绍了一种混合深度学习模型,用于准确预测零售销售. 通过将空间和时间模式与外部因素相结合,它可以显著提高预测准确度,从而更好地做出商业决策.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 零售分析 零售分析
背景情况:
- 准确的销售预测对于零售利能力和库存管理至关重要.
- 现有的深度学习模型经常单独分析时间或空间数据,从而限制了现实世界的准确性.
- 许多研究使用合成数据或排除关键的上下文变量.
研究的目的:
- 开发一种混合深度学习模型,用于增强零售销售预测.
- 将环境和人口变量纳入销售预测模型.
- 提高零售业销售预测的准确性和可靠性.
主要方法:
- 提出了一个混合卷积神经网络 (CNN) 长短记忆 (LSTM) 模型.
- 该模型将空间模式识别 (CNN) 与时间依赖模型 (LSTM) 集成在一起.
- 现实世界零售数据增加了诸如假日,工资日,抗议活动和天气状况等外部因素.
主要成果:
- 混合CNN-LSTM模型实现了4.16%的平均绝对百分比误差 (MAPE).
- 拟议的模型显著优于传统和独立的神经网络模型.
- 整合外部变量提高了模型的预测性能.
结论:
- 混合CNN-LSTM模型为多变量零售销售预测提供了强大的解决方案.
- 整合多种数据源可以提高预测可靠性,并支持知情的零售业务.
- 这种方法为更准确的供需平衡和提高利能力提供了基础.
相关概念视频
01:18Microsoft Excel: Regression Analysis
1.5K
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
To perform regression...
1.5K
01:29Application of Differentiation to Business
48
Calculus offers essential techniques for businesses seeking to optimize pricing strategies and revenue. In this case, a bakery wants to determine the ideal price and daily sales volume to maximize revenue. By modeling how changes in price affect demand and revenue, the bakery can apply calculus to make data-driven decisions.The demand function relates the price per cupcake to the number of cupcakes sold and captures how lower prices increase sales. Based on market data, the demand function can...
48