Research on Apparel Trend Prediction Based on CNN-BiLSTM-Attention Model

Abstract

The existing methods for forecasting clothing trends mostly use traditional time series forecasting methods, and the data sources are mostly sale data from e-commerce websites, which have large errors in forecasting accuracy. This paper proposes a new model CNN-BiLSTM-Attention for predicting clothing trends based on social media data. The Geostyle dataset is pre-processed to get the clothing popularity index. First, One-dimensional CNN is used to extract the important features in the clothing popularity index. Second, the BiLSTM is used to make full use of contextual information. Third, adding an Attention mechanism to the output can highlight relevant information, suppress irrelevant information, and significantly improve prediction accuracy. The experimental results show that our method is significantly better than other traditional time series forecasting methods and existing deep learning methods when applied to apparel trend forecasting.