论文标题
使用机器学习方法的需求预测并堆叠概括
Demand Prediction Using Machine Learning Methods and Stacked Generalization
论文作者
论文摘要
供求是卖方和客户的两个基本概念。准确地预测需求对于组织才能制定计划至关重要。在本文中，我们在电子商务网站上提出了一种新的需求预测方法。所提出的模型与早期模型有多种不同。电子商务网站中使用的业务模型，该网站为其实施该模型，其中包括许多卖家，这些卖家以公司运营市场模型的不同价格同时出售同一产品。这种模型的需求预测应考虑通过这些卖家的功能，由竞争卖家出售的同一产品的价格。在这项研究中，我们首先将不同的回归算法应用于一家公司的特定产品集，该公司是土耳其最受欢迎的在线电子商务公司之一。然后，我们使用堆叠的概括或称为堆叠集合学习来预测需求。最后，所有方法均在从电子商务公司获得的现实世界数据集上进行评估。实验结果表明，某些机器学习方法的产生的结果几乎与堆叠的概括方法一样好。
Supply and demand are two fundamental concepts of sellers and customers. Predicting demand accurately is critical for organizations in order to be able to make plans. In this paper, we propose a new approach for demand prediction on an e-commerce web site. The proposed model differs from earlier models in several ways. The business model used in the e-commerce web site, for which the model is implemented, includes many sellers that sell the same product at the same time at different prices where the company operates a market place model. The demand prediction for such a model should consider the price of the same product sold by competing sellers along the features of these sellers. In this study we first applied different regression algorithms for specific set of products of one department of a company that is one of the most popular online e-commerce companies in Turkey. Then we used stacked generalization or also known as stacking ensemble learning to predict demand. Finally, all the approaches are evaluated on a real world data set obtained from the e-commerce company. The experimental results show that some of the machine learning methods do produce almost as good results as the stacked generalization method.
