Grocery Sales Forecasting

DOI

10.2991/aebmr.k.220404.040How to use a DOI?

Keywords

Machine learning; sale prediction; time series; LGBM; regression

Abstract

Forecasting grocery items can not only avoid excessive stockpiling but also meet customer demand. This can reduce the grocery store’s losses and increase the grocery store’s turnover. On the other hand, considering features that affect sales is also the core of feature engineering. This article makes a forecast about the sales of merchandise in a large chain store. In this paper, two cores of time series and LGBM are mainly used to complete the model establishment. A model that can predict the sales of goods is built. The data used for training is analyzed and processed. The accuracy of the model is measured using the mean squared error, which gives a final accuracy of 0.35069696616549817. At the end of the paper, it proposes an improved method for this model, and how to solve the same problem under other conditions (such as when the data is particularly small).

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

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TY  - CONF
AU  - Yixu Liu
PY  - 2022
DA  - 2022/04/18
TI  - Grocery Sales Forecasting
BT  - Proceedings of the 2022 International Conference on Creative Industry and Knowledge Economy (CIKE 2022)
PB  - Atlantis Press
SP  - 215
EP  - 219
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220404.040
DO  - 10.2991/aebmr.k.220404.040
ID  - Liu2022
ER  -