Time Series Clustering for Grouping Products Based on Price and Sales Patterns

• URL: http://arxiv.org/abs/2204.08334v1
• Date: Mon, 18 Apr 2022 14:24:42 GMT
• Title: Time Series Clustering for Grouping Products Based on Price and Sales Patterns
• Authors: Aysun Bozanta, Sean Berry, Mucahit Cevik, Beste Bulut, Deniz Yigit, Fahrettin F. Gonen, and Ay\c{s}e Ba\c{s}ar
• Abstract summary: We propose a novel distance metric that takes into account how product prices and sales move together. We evaluate the performances of different clustering algorithms using our custom evaluation metric and Calinski Harabasz and Davies Bouldin indices.
• Score: 0.3518016233072556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 the 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 price 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 the products with similar price and sales patterns within large datasets.

Related papers

• Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language Models [50.370043676415875]
  In smart retail applications, the large number of products and their frequent turnover necessitate reliable zero-shot object classification methods. We introduce the MIMEX dataset, comprising 28 distinct product categories. We benchmark the zero-shot object classification performance of state-of-the-art vision-language models (VLMs) on the proposed MIMEX dataset.
  arXiv  Detail & Related papers  (2024-09-23T12:28:40Z)
• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market [0.0]
  We aim to develop a customer segmentation model to improve decision-making processes in the retail market industry. To achieve this, we employed a UK-based online retail dataset obtained from the UCI machine learning repository. The results showed the GMM outperformed other approaches, with a Silhouette Score of 0.80.
  arXiv  Detail & Related papers  (2024-02-06T15:58:14Z)
• Overview of the TREC 2023 Product Product Search Track [70.56592126043546]
  This is the first year of the TREC Product search track. The focus was the creation of a reusable collection. We leverage the new product search corpus, which includes contextual metadata.
  arXiv  Detail & Related papers  (2023-11-14T02:25:18Z)
• Improved Sales Forecasting using Trend and Seasonality Decomposition with LightGBM [9.788039182463768]
  We propose a new measure to indicate the unique impacts of the trend and seasonality components on a time series. Our experiments show that the proposed strategy can achieve improved accuracy.
  arXiv  Detail & Related papers  (2023-05-26T18:49:42Z)
• Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape [1.0923877073891446]
  This paper presents a comprehensive study that employs various time-series forecasting methods to predict daily average CPC in the online advertising market. We evaluate the performance of statistical models, machine learning techniques, and deep learning approaches, including the Temporal Fusion Transformer (TFT)
  arXiv  Detail & Related papers  (2023-02-11T19:26:17Z)
• Learning Consumer Preferences from Bundle Sales Data [2.6899658723618005]
  We propose an approach to learn the distribution of consumers' valuations toward the products using bundle sales data. Using the EM algorithm and Monte Carlo simulation, our approach can recover the distribution of consumers' valuations.
  arXiv  Detail & Related papers  (2022-09-11T21:42:49Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• Approaching sales forecasting using recurrent neural networks and transformers [57.43518732385863]
  We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques. Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort. The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition.
  arXiv  Detail & Related papers  (2022-04-16T12:03:52Z)
• Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting [63.78344280962136]
  Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level. This work seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner.
  arXiv  Detail & Related papers  (2020-09-08T12:10:10Z)
• Scalable bundling via dense product embeddings [1.933681537640272]
  Bundling is the practice of jointly selling two or more products at a discount. We develop a new machine-learning-driven methodology for designing bundles in a large-scale, cross-category retail setting. We find that our embeddings-baseds are strong predictors of bundle success, robust across product, and generalize well to the retailer's entire assortment.
  arXiv  Detail & Related papers  (2020-01-31T23:34:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.