Relational analysis model of weather conditions and sales patterns based on nonnegative tensor factorization

Abstract

It is necessary to analyze the relationships between the retail sales of various items and weather conditions. However, the relationship between the sales of each item and the weather condition may vary among stores. Additionally, it is necessary to model the statistical relationships between a wide variety of goods and weather conditions by using past sales data. In such a case, it becomes unrealistic to construct a forecast model for every individual item owing to the breadth of items and the number of retail shops. This study proposes a model to analyze the relationships between the sales of various items and weather conditions. This method can be used to decompose the data into three matrices based on the nonnegative tensor factorization (NTF) method. The results of the analysis clarified that the proposed model can identify important items whose demand is strongly influenced by weather conditions, thereby increasing the effectiveness of inventory management. Additionally, the store clusters estimated by the proposed model can facilitate the construction of regression models that demonstrate the relationship between the sales of each item and weather conditions.

Keywords:

• weather
• weather data
• clustering
• business analytics
• nonnegative tensor factorization
• nonnegative matrix factorization

Notes

1 The analytical model based on NMF was presented in Japanese at the conference of the Japan Industrial Management Association (Okayama et al. 2016).