Review on Fashion Trend Analysis and Forecasting Techniques - A Machine Learning Approach

The fashion industry is characterized by its rapid changes involving consumer’s ever-changing preferences, whether in color palettes, patterns, seasonal trends, or social and cultural influences. Fashion reflects one’s culture, individuality, and societal trends. With technological advancement, the fashion generation and recommendation systems have shown enhancements. The integration of advanced algorithms has emerged as a transformative solution, capable of uncovering concealed insights and effectively addressing color variations and pattern formulation within the intricate landscape of fashion collections. This transformative approach significantly influences production processes and design strategies, fostering an environment where adaptability and responsiveness become pivotal for success. With the rise of machine learning, the industry experiences a paradigm shift, gaining an unprecedented ability to analyze historical and real-time data. The analysis gives a clear idea about the possible combinations and can be efficiently used to create designs that match the upcoming trends. This study examines the use of machine learning as well as deep learning and artificial intelligence in the fashion industry for tasks such as clothing recognition, style understanding, color and style extraction, outfit recommendations, and fashion forecasting. The study highlights various ways of applying machine learning in the fashion domain. However, it’s important to note that this review only covers a few methods, as they have shown the best performance in accuracy and efficiency when dealing with a vast amount of fashion data. The limitations mentioned suggest areas that still need exploration for future research.