Comparative Study of AI-Driven Fashion Trend Prediction System Using AI and ML: A Review

Year : 2025 | Volume : 12 | Issue : 02 | Page : 35 41
    By

    Shruti Vivekanand Chavan,

  • Janhavi Santosh Pardeshi,

  • Shubhangi Nivrutti Pingle,

  • Raksha Sandip Sonawane,

  • Vipin K. Wani,

  1. Student, Department of Computer, MET’s Institute of Engineering, Bhujbal Knowledge City, College in Nashik, Maharashtra, India
  2. Student, Department of Computer, MET’s Institute of Engineering, Bhujbal Knowledge City, College in Nashik, Maharashtra, India
  3. Student, Department of Computer, MET’s Institute of Engineering, Bhujbal Knowledge City, College in Nashik, Maharashtra, India
  4. Student, Department of Computer, MET’s Institute of Engineering, Bhujbal Knowledge City, College in Nashik, Maharashtra, India
  5. Assistant Professor, Department of Computer, MET’s Institute of Engineering, Bhujbal Knowledge City, College in Nashik, Maharashtra, India

Abstract

To overcome the challenges in fashion trend forecasting, researchers have introduced several advanced and data-driven approaches. One such method uses a long short-term memory (LSTM) model combined with an encoder-decoder architecture to extract meaningful fashion content and recognize styles from product images. This model achieves higher accuracy in predicting upcoming fashion trends by incorporating varying price intervals and has shown impressive results when evaluated on the Amazon fashion dataset. Another forecasting model focuses on analyzing the evolution of fashion styles over time. It can detect and predict new combinations of styles, identify recurring patterns, and distinguish between emerging trends and timeless classics. This model’s effectiveness has been validated using a dataset of 80,000 fashion products sold on Amazon over a period of 6 years. In addition, the Fashion Attributes Recognition Network (FARNet) has been developed to enhance attribute prediction by simultaneously identifying multiple fashion attributes and correcting noisy labels, which commonly occur in large-scale datasets. FARNet has demonstrated substantial improvements over previous methods and has been applied to the RichWear dataset, comprising over 322,000 images sourced from an Asian social media platform. Through image clustering and refined label prediction, FARNet has proven useful in identifying regional and street fashion trends, especially within Asian markets. Together, these models represent a significant advancement in the ability to predict, analyze, and adapt to changing fashion landscapes using deep learning and large-scale visual data.

Keywords: Artificial intelligence (AI), machine learning (ML), long short-term memory (LSTM), industry, fashion attributes recognition network (FARNet)

[This article belongs to E-Commerce for Future & Trends ]

How to cite this article:
Shruti Vivekanand Chavan, Janhavi Santosh Pardeshi, Shubhangi Nivrutti Pingle, Raksha Sandip Sonawane, Vipin K. Wani. Comparative Study of AI-Driven Fashion Trend Prediction System Using AI and ML: A Review. E-Commerce for Future & Trends. 2025; 12(02):35-41.
How to cite this URL:
Shruti Vivekanand Chavan, Janhavi Santosh Pardeshi, Shubhangi Nivrutti Pingle, Raksha Sandip Sonawane, Vipin K. Wani. Comparative Study of AI-Driven Fashion Trend Prediction System Using AI and ML: A Review. E-Commerce for Future & Trends. 2025; 12(02):35-41. Available from: https://journals.stmjournals.com/ecft/article=2025/view=209496


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Regular Issue Subscription Original Research
Volume 12
Issue 02
Received 03/03/2025
Accepted 28/04/2025
Published 05/05/2025
Publication Time 63 Days
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