Comparative Analysis of Modern Machine Learning Models for Retail Sales Forecasting

• URL: http://arxiv.org/abs/2506.05941v1
• Date: Fri, 06 Jun 2025 10:08:17 GMT
• Title: Comparative Analysis of Modern Machine Learning Models for Retail Sales Forecasting
• Authors: Luka Hobor, Mario Brcic, Lidija Polutnik, Ante Kapetanovic,
• Abstract summary: When forecasts underestimate the level of sales, firms experience lost sales, shortages, and impact on the reputation of the retailer in their relevant market.<n>This study provides an exhaustive assessment of the forecasting models applied to a high-resolution brick-and-mortar retail dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate forecasting is key for all business planning. When estimated sales are too high, brick-and-mortar retailers may incur higher costs due to unsold inventories, higher labor and storage space costs, etc. On the other hand, when forecasts underestimate the level of sales, firms experience lost sales, shortages, and impact on the reputation of the retailer in their relevant market. Accurate forecasting presents a competitive advantage for companies. It facilitates the achievement of revenue and profit goals and execution of pricing strategy and tactics. In this study, we provide an exhaustive assessment of the forecasting models applied to a high-resolution brick-and-mortar retail dataset. Our forecasting framework addresses the problems found in retail environments, including intermittent demand, missing values, and frequent product turnover. We compare tree-based ensembles (such as XGBoost and LightGBM) and state-of-the-art neural network architectures (including N-BEATS, NHITS, and the Temporal Fusion Transformer) across various experimental settings. Our results show that localized modeling strategies especially those using tree-based models on individual groups with non-imputed data, consistently deliver superior forecasting accuracy and computational efficiency. In contrast, neural models benefit from advanced imputation methods, yet still fall short in handling the irregularities typical of physical retail data. These results further practical understanding for model selection in retail environment and highlight the significance of data preprocessing to improve forecast performance.

Related papers

• LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data [63.777637042161544]
  This paper introduces a novel forecast post-processor that fine-tunes large language models to incorporate unstructured semantic and contextual information and historical data.<n>In an industry-scale retail application, we demonstrate that our technique yields statistically significantly forecast improvements across several sets of products subject to holiday-driven demand surges.
  arXiv  Detail & Related papers  (2024-12-03T16:18:42Z)
• Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy [104.48511402784763]
  Performance Law for SR models aims to theoretically investigate and model the relationship between model performance and data quality.<n>We propose Approximate Entropy (ApEn) to assess data quality, presenting a more nuanced approach compared to traditional data quantity metrics.
  arXiv  Detail & Related papers  (2024-11-30T10:56:30Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition [99.7047087527422]
  In this work, we demonstrate that competition can fundamentally alter the behavior of machine learning scaling trends. We find many settings where improving data representation quality decreases the overall predictive accuracy across users. At a conceptual level, our work suggests that favorable scaling trends for individual model-providers need not translate to downstream improvements in social welfare.
  arXiv  Detail & Related papers  (2023-06-26T13:06:34Z)
• Multimodal Neural Network For Demand Forecasting [0.8602553195689513]
  We propose a multi-modal sales forecasting network that combines real-life events from news articles with traditional data such as historical sales and holiday information. We show statistically significant improvements in the SMAPE error metric with an average improvement of 7.37% against the existing state-of-the-art sales forecasting techniques.
  arXiv  Detail & Related papers  (2022-10-20T18:06:36Z)
• 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)
• A Comparative Study on Forecasting of Retail Sales [0.0]
  We benchmark forecasting models on historical sales data from Walmart to predict their future sales. We apply these models on the forecasting challenge dataset (M5 forecasting by Kaggle)
  arXiv  Detail & Related papers  (2022-03-14T04:24:29Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• Predicting seasonal influenza using supermarket retail records [59.18952050885709]
  We consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence.
  arXiv  Detail & Related papers  (2020-12-08T16:30:43Z)
• How Much Can A Retailer Sell? Sales Forecasting on Tmall [20.51263243320733]
  We study the case of retailers' sales forecasting on Tmall|the world's leading online B2C platform. We design two mechanisms for sales forecasting, i.e., seasonality extraction and distribution transformation.
  arXiv  Detail & Related papers  (2020-02-27T06:41:00Z)
• Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective [3.613072342189595]
  This paper compares a large array of techniques for 35 times series that consist of both industry data from the Coca-Cola Company and publicly available datasets. It introduces a novel and completely automated profit-driven approach that takes into account the expected profit that a technique can create during both the model building and evaluation process.
  arXiv  Detail & Related papers  (2020-02-03T14:50:24Z)

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.