Competition between times series often arises in sales prediction, when similar products are on sale on a marketplace. This article provides a model of the presence of cannibalization between times series. This model creates a "competitiveness" function that depends on external features such as price and margin. It also provides a theoretical guaranty on the error of the model under some reasonable conditions, and implement this model using a neural network to compute this competitiveness function. This implementation outperforms other traditional time series methods and classical neural networks for market share prediction on a real-world data set.
翻译:当类似产品在市场上销售时,在销售预测中经常出现时间序列之间的竞争。 本条提供了一个在时间序列之间进行拆解的模型。 这一模型创造了一种取决于价格和差值等外部特征的“ 竞争性” 功能。 该模型还在某些合理条件下为模型的错误提供了理论上的保证, 并利用神经网络应用这一模型来计算这一竞争力功能。 这一执行超过了其他传统时间序列方法和传统神经网络, 用于在真实世界数据集中进行市场份额预测。