mlsanityData to 14 Oct 2026

Deep Learning based Forecasting: a case study from the online fashion industry

arXiv 2305.14406 ↗First posted 23 May 2023cs.LG cs.AIcited by 12

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Abstract (arXiv)

Demand forecasting in the online fashion industry is particularly amendable to global, data-driven forecasting models because of the industry's set of particular challenges. These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption. While standard deep learning forecasting approaches cater for many of these, the fixed inventory assumption requires a special treatment via controlling the relationship between price and demand closely. In this case study, we describe the data and our modelling approach for this forecasting problem in detail and present empirical results that highlight the effectiveness of our approach.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 34 of its 56 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

34 of 56 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 2; 2025: 4; 2026: 1.

12 papers.

  1. 2023TimeGPT-1cited by 158
  2. 2023GIVT: Generative Infinite-Vocabulary Transformerscited by 80
  3. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  4. 2023Hierarchical Forecasting at Scalecited by 4
  5. 2023Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approachcited by 4
  6. 2024Time-Series Foundation Models for Forecasting Soil Moisture Levels in Smart Agriculturecited by 1
  7. 2025Hierarchical Time Series Forecasting Via Latent Mean Encodingcited by 1
  8. 2024Magazine Supply Optimization: a Case-studycited by 0
  9. 2025Learning Joint Interventional Effects from Single-Variable Interventions in Additive Modelscited by 0
  10. 2025Comparative Analysis of Modern Machine Learning Models for Retail Sales Forecastingcited by 0
  11. 2025Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecastingcited by 0
  12. 2026High-Frequency Pricing at Scale for E-Commercecited by 0