Textile and Apparel Industries

Neo-Fashion: A Data-Driven Fashion Trend Forecasting System using Machine Learning through Catwalk Analysis

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Abstract

Trend forecasting is crucially important and challenging in the fashion industry (Bikhchandani, S., Hirshleifer, D., & Welch,1992),and recently has been an emerging research area in computer vision and machine learning (Vittayakorn et al., 2015; Liu et al., 2016; Han et al., 2017). In the fashion world, trend forecasting is defined as the search for a means to predict mood, behavior, and buying habits of the consumer through identifying trends (Halland & Jones, 2017). With the advent of computational approach, it’s possible to translate the creativity and inspiration of practitioners into a data-driven structure, especially for short-term forecasting which is the main focus of this study.

Keywords: Autonomous Prediction, Catwalk Analysis, Recommendation System, Deep Neural Network, Data-Driven Fashion Trends Forecasting

How to Cite:

Zhao, L., Li, M. & Sun, P., (2020) “Neo-Fashion: A Data-Driven Fashion Trend Forecasting System using Machine Learning through Catwalk Analysis”, International Textile and Apparel Association Annual Conference Proceedings 77(1). doi: https://doi.org/10.31274/itaa.12062