Data-driven Fashion Forecasting

Data-Driven Fashion Forecasting is a predictive methodology within fashion trend forecasting that systematically leverages quantitative data from diverse digital sources, distinguished by its reliance on statistical analysis, machine learning algorithms, and real-time consumer behavior patterns to identify and project emerging fashion trends.

44

Publications

2.9K

Citations

101

Authors

54

Institutions

Publications per year

2017–2026

29

Authors

101

  1. TC
    Tsan‐Ming Choi

    Hong Kong Polytechnic University, Hong Kong SAR China

    8 publications · 1.1K citations

    8

    h-index

  2. YY
    Yong Yu

    Kagoshima University, Japan

    6 publications · 908 citations

    6

    h-index

  3. CL
    Chi Leung Hui

    Hong Kong Polytechnic University, Hong Kong SAR China

    4 publications · 332 citations

    4

    h-index

  4. KG
    Kristen Grauman

    The University of Texas at Austin, United States

    3 publications · 208 citations

    3

    h-index

  5. KA
    Kin-Fan Au

    Hong Kong Polytechnic University, Hong Kong SAR China

    2 publications · 576 citations

    2

    h-index

1–5 of 101

Venues

  1. 3

    h-index

  2. International Journal of Production Economics

    2 publications · 334 citations

    2

    h-index

  3. Decision Support Systems

    2 publications · 567 citations

    2

    h-index

  4. Expert Systems with Applications

    2 publications · 167 citations

    2

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 40 citations

    2

    h-index

1–5