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Sales forecasting for computer wholesalers: A comparison of multivariate adaptive regression splines and artificial neural networks

机译：计算机批发商的销售预测：多元自适应回归样条和人工神经网络的比较

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摘要

Artificial neural networks (ANNs) have been found to be useful for sales/demand forecasting. However, one of the main shortcomings of ANNs is their inability to identify important forecasting variables. This study uses multivariate adaptive regression splines (MARS), a nonlinear and non-parametric regression methodology, to construct sales forecasting models for computer wholesalers. Through the outstanding variable screening ability of MARS, important sales forecasting variables for computer wholesalers can be obtained to enable them to make better sales management decisions. Two sets of real sales data collected from Taiwanese computer wholesalers are used to evaluate the performance of MARS. The experimental results show that the MARS model outperforms backpropagation neural networks, a support vector machine, a cerebellar model articulation controller neural network, an extreme learning machine, an ARIMA model, a multivariate linear regression model, and four two-stage forecasting schemes across various performance criteria. Moreover, the MARS forecasting results provide useful information about the relationships between the forecasting variables selected and sales amounts through the basis functions, important predictor variables, and the MARS prediction function obtained, and hence they have important implications for the implementation of appropriate sales decisions or strategies.
机译：人工神经网络（ANN）已发现对销售/需求预测很有用。但是，人工神经网络的主要缺点之一是无法识别重要的预测变量。这项研究使用多元自适应回归样条（MARS），一种非线性和非参数回归方法，为计算机批发商构建销售预测模型。通过MARS出色的变量筛选能力，可以获得针对计算机批发商的重要销售预测变量，以使他们能够做出更好的销售管理决策。从台湾的计算机批发商那里收集的两组实际销售数据用于评估MARS的性能。实验结果表明，MARS模型的性能优于反向传播神经网络，支持向量机，小脑模型关节控制器神经网络，极限学习机，ARIMA模型，多元线性回归模型以及跨两个阶段的四个两阶段预测方案性能标准。而且，MARS预测结果通过基本函数，重要的预测变量和获得的MARS预测函数，提供了有关所选预测变量与销售额之间关系的有用信息，因此，它们对于实施适当的销售决策或策略。

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