Embedding-Based Potential Sales Forecasting of Bread Product

Kohei Takahashi, Yusuke Goto

研究成果: Article査読

抄録

In this study, we investigate the potential sales forecasts of unhandled bread products in retail stores based on factory shipment data. An embedding-based forecasting method that uses large-scale information network embedding (LINE) and simultaneously considers first- and second-order proximities is developed to define similar neighboring stores using their product-store relationship and to predict their potential sales volume. LINE is a network-embedding method that transforms network data into a lowdimensional distributed representation and requires a low computation time, even when applied to large networks. The results show that our proposed method outperforms a simple prediction method (Baseline) and t-SNE, a well-known dimensionality reduction method for high-dimensional data, in terms of accurate product sales prediction via simulation experiments. Furthermore, we conduct a sensitivity analysis to verify the applicability of our proposed method when the forecasting target is expanded to products sold in fewer stores and in stores with less product variety.

本文言語English
ページ(範囲)236-246
ページ数11
ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
26
2
DOI
出版ステータスPublished - 2022 3月
外部発表はい

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • 人工知能

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