JACIII Vol.26 No.2 pp. 236-246
doi: 10.20965/jaciii.2022.p0236


Embedding-Based Potential Sales Forecasting of Bread Product

Kohei Takahashi* and Yusuke Goto**

*Graduate School of Software and Information Science, Iwate Prefectural University
152-52 Sugo, Takizawa, Iwate 020-0693, Japan

**College of Systems Engineering and Science, Shibaura Institute of Technology
307 Fukasaku, Minuma-ku, Saitama, Saitama 337-8570, Japan

July 30, 2021
February 7, 2022
March 20, 2022
network embedding, forecasting, bread, bipartite graph

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 low-dimensional 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.

Flow of forecasting simulation experiment

Flow of forecasting simulation experiment

Cite this article as:
K. Takahashi and Y. Goto, “Embedding-Based Potential Sales Forecasting of Bread Product,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.2, pp. 236-246, 2022.
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Last updated on May. 19, 2024