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University Restaurant Sales Forecast Based on BP Neural Network - In Shanghai Jiao Tong University Case

机译:基于BP神经网络的高校饭店销售预测-以上海交通大学为例

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In recent years, BP (Back Propagation) neural network is widely used in predictive modeling in various fields. But the BP neural network technology which used for university catering service is very few. The article is applied to the data set which is published by the EMC competition of Shanghai Jiao Tong University in 2015. We use BP neural network to analyze and forecast the university restaurant sales, and then through comparing the model with the time series forecasting method. The elements used in the model include the cycle factor, the Baidu index of the network take away, the weather information. The forecasting factors include three aspects of the 11 variables, which is also an innovation of this paper. Finally, we proved that the model we built has a good prediction result and it also has practical availability. This article also explained how the variables impact on university catering service.
机译:近年来,BP(反向传播)神经网络被广泛用于各个领域的预测建模中。但是用于大学餐饮服务的BP神经网络技术很少。本文适用于上海交通大学EMC竞赛2015年发布的数据集。我们使用BP神经网络对大学饭店的销售进行分析和预测,然后将该模型与时间序列预测方法进行比较。该模型中使用的元素包括循环因子,网络的百度带走指数,天气信息。预测因素包括11个变量的三个方面,这也是本文的创新之处。最后,我们证明了所建立的模型具有良好的预测结果,并且具有实际可用性。本文还解释了变量如何影响大学餐饮服务。

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