Food Sales Prediction with Meteorological Data — A Case Study of a Japanese Chain Supermarket

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10387)

Abstract

The weather has a strong influence on food retailers’ sales, as it affects customers emotional state, drives their purchase decisions, and dictates how much they are willing to spend. In this paper, we introduce a deep learning based method which use meteorological data to predict sales of a Japanese chain supermarket. To be specific, our method contains a long short-term memory (LSTM) network and a stacked denoising autoencoder network, both of which are used to learn how sales changes with the weathers from a large amount of history data. We showed that our method gained initial success in predicting sales of some weather-sensitive products such as drinks. Particularly, our method outperforms traditional machine learning methods by 19.3%.

Keywords

Sales prediction LSTM Autoencoder Meteorological data 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.National Institute of InformaticsTokyoJapan

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