Traditional Versus Big-Data Based Fashion Trend Forecasting: An Examination Using WGSN and EDITED
The purpose of this empirical study is to compare the similarities and differences of the results of traditional human-based fashion trend forecasts with the ones generated by big data. The result confirms the overall feasibility and great potential of using big-data tools to forecast fashion trend as a creative activity. Second, the findings suggest that big-data tools have the strengths in forecasting color and apparel pattern, but may not be most effective in predicting design details, which seem to be shaped by more unpredictable and complex factors.
Keywords: Big data, fashion trend forecast, WGSN, EDITED, Business analytics
How to Cite:
DuBreuil, M. & Lu, S., (2019) “Traditional Versus Big-Data Based Fashion Trend Forecasting: An Examination Using WGSN and EDITED”, International Textile and Apparel Association Annual Conference Proceedings 76(1). doi: https://doi.org/10.31274/itaa.8246