Dynamic Knowledge Enhanced Neural Fashion Trend Forecasting with Quantile Loss

Forecasting fashion trends based on time series and style analysis is an innovative and attractive approach. Still, the dynamic nature of fashion trends and the influence of various factors make this forecast challenging. Additionally, it’s important to note that previous research in this field has primarily focused on specific elements, such as seasonal clothes, which may not be suitable to the evolving requirements of the fashion industry. So, we extended the Knowledge Enhanced Recurrent Network. (KERN) model, which leverages deep recurrent neural networks for time series analysis to forecast fine-grained elements for a specific user group. Due to the low flexibility and the lack of management of sequences with variable memory lengths in the KERN method and the increasing improvement of memory the efficiency of this model, we propose a Dynamic Knowledge Enhanced Recurrent Network model (DKERN) along with the use of the Quantile loss function, which is an improvement on this method. We have tried to help the fashion industry by improving forecasting fashion trends by using various analytical techniques. The proposed approaches have shown improvements ranging between 0.2% to 8.2% compared to previous works in terms of periods, datasets, and different evaluation metrics.