TITLE:
Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning
AUTHORS:
Jieni Lv, Shuguang Han, Jueliang Hu
KEYWORDS:
Clothing Retail, Sales Forecasting, Weather, Machine-Learning, Stacking
JOURNAL NAME:
Journal of Textile Science and Technology,
Vol.9 No.1,
February
7,
2023
ABSTRACT: Reliable sales forecasts are important to the garment industry. In recent
years, the global climate is warming, the weather changes frequently, and
clothing sales are affected by weather fluctuations. The purpose of this study
is to investigate whether weather data can improve the accuracy of product
sales and to establish a corresponding clothing sales forecasting model. This
model uses the basic attributes of clothing product data, historical sales
data, and weather data. It is based on a random forest, XGB, and GBDT adopting
a stacking strategy. We found that weather information is not useful for basic
clothing sales forecasts, but it did improve the accuracy of seasonal clothing
sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced
by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the
stacking strategy model outperformed the voting strategy model, with an average
MSE reduction of 49.28%. Clothing managers can use this model to forecast their
sales when they make sales plans based on weather information.