Time series clustering for grouping products based on price and sales patterns
Information Technology and Management, cilt.27, sa.1, ss.125-139, 2026 (SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 27 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s10799-025-00455-4
- Dergi Adı: Information Technology and Management
- Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, ABI/INFORM, INSPEC, Library, Information Science & Technology Abstracts (LISTA)
- Sayfa Sayıları: ss.125-139
- Anahtar Kelimeler: Time series clustering, Image clustering, Machine learning, Unsupervised learning, Product pricing and sales
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Developing technology and changing lifestyles have made online grocery delivery applications an indispensable part of urban life. Since the beginning of the COVID-19 pandemic, the demand for such applications has dramatically increased, creating new competitors that disrupt the market. An increasing level of competition might prompt companies to frequently restructure their marketing and product pricing strategies. Therefore, identifying the change patterns in product prices and sales volumes would provide a competitive advantage for the companies in the marketplace. In this paper, we investigate alternative clustering methodologies to group the products based on price patterns and sales volumes. We propose a novel distance metric that takes into account how product prices and sales move together rather than calculating the distance using numerical values. We compare our approach with traditional clustering algorithms, which typically rely on generic distance metrics such as Euclidean distance, and image clustering approaches that aim to group data by capturing its visual patterns. We evaluate the performances of different clustering algorithms using our custom evaluation metric as well as Calinski Harabasz and Davies Bouldin indices, which are commonly used internal validity metrics. We conduct our numerical study using a propriety Getir dataset from an online food and grocery delivery company, and the publicly available Favorita sales dataset. We find that our proposed clustering approach and image clustering both perform well for finding products with similar price and sales patterns within large datasets.