A Predictive Model of Seasonal Clothing Demand with Weather Factors

Oh, Jun-Hyok; Ha, Kyung‐Ja; Jo, Young‐Heon

doi:10.1007/s13143-022-00284-3

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…Secondly, it is necessary to understand the demand for talent in different industries, fully considering the current technological requirements, market demand, salary situation, and ability requirements of different industries. By analyzing the talent needs of industries, higher education institutions can optimize their departments and professional settings, thereby improving the pertinence and practicality of talent cultivation, making it easier for graduates to adapt to the industry needs of their majors, and providing talent support for the development of industries [7][8]. Finally, it is necessary for higher education institutions to maintain a close connection with industry demand when setting up majors.…”

Section: Industry Demandmentioning

confidence: 99%

Research on the Direction Optimization of Professional Cluster in Higher Education Institutions Based on Industrial Demand

2024

IJNDE

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Due to the rapid development of the economy and the continuous progress of social technology, the alignment between the professional settings of higher education institutions and industrial demands has become a hot research topic. In order to explore how to better meet the industrial demand for higher education in society, this paper conducted research through the analysis of industrial demand and the optimization of professional clusters in higher education institutions. This article first conducted an in-depth analysis of the development trend of the industry, then conducted optimization research on the direction of professional clusters in higher education institutions, and finally constructed a random forest model for optimization. Through experimental data, it has been proven that the optimized direction of professional clusters in higher education institutions can meet the current industrial needs and increase the employment rate by about 20.8%. This study provided useful references and suggestions for the development of universities and society, promoting social progress and development.

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Section: Industry Demandmentioning

confidence: 99%

Research on the Direction Optimization of Professional Cluster in Higher Education Institutions Based on Industrial Demand

2024

IJNDE

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“…When studying the impact of weather on sales, Martinez-de-Albeniz and Belkaid [3] took additional factors such as location, season, and product category, and proved with case data that retailers could increase revenue by 2% through weather pricing. Oh et al [7] used the Google search data (WGT) of winter jackets to build a generalized linear mixed (GLMM) seasonal clothing demand model, and decomposed the linear relationship between the wind chill data with a time lag and the monthly WGT into random effects over many years. GLMM is an extended form of the ordinary linear model, which extends the distribution of dependent variables, so that the dependent variables only obey the exponential distribution family, and increases the range of weather data that can be used.…”

Section: Research Progress On the Influence Of Weather On Sales Forecastmentioning

confidence: 99%

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

Lv¹,

Han²,

Hu³

2023

JTST

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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.

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