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

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Data Mining and Big Data (DMBD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10387))

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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%.

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Notes

  1. 1.

    Due to privacy issues, the supermarket’s name has been omitted.

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Acknowledgment

We gratefully thank Japan Weather Association especially Mr.Tomohiro Yoshikai for supporting our work.

Author information

Authors and Affiliations

  1. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan

    Xin Liu & Ryutaro Ichise

Authors
  1. Xin Liu
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  2. Ryutaro Ichise
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Corresponding author

Correspondence to Xin Liu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Liu, X., Ichise, R. (2017). Food Sales Prediction with Meteorological Data — A Case Study of a Japanese Chain Supermarket. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-61845-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61844-9

  • Online ISBN: 978-3-319-61845-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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