Gnnctd: A Graph Neural Network Based on Complicated Temporal Dependencies Modeling for Fashion Trend Prediction

Abstract

Fashion trend forecasting has consistently remained a focal point within the realm of fashion. Existing methods predominantly concentrate on the external factors influencing fashion trends, often disregarding the intricate interplay among distinct fashion elements, namely the "spatial dependencies" among them. It is also a significant challenge to excavate complicated temporal relationships in complex time series data. In this research, our primary focus is modeling the relationships among diverse fashion elements and the intricate temporal dependencies within time series data. First, we propose a Street Fashion Trend (SFT) dataset by leveraging images from the popular photo-sharing platform, Flickr. Furthermore, we propose a model named GNNctd to solve the above problems. This model leverages a spatial dependency capture module (SDCM) based on graph neural network to dynamically model the "spatial dependency relationships" among distinct fashion elements. Meanwhile, the model introduces a temporal relationship extraction block (TREB), which comprises two pivotal modules: the interaction learning (IL) module designed to capture local temporal dependencies and a global time attention module (GTAM) which is used to capture global temporal dependencies. Many experiments substantiate that the proposed GNNctd model can achieve more accurate predictions for the fashion trend in our constructed dataset. Simultaneously, the GNNctd model achieves state-of-the-art performance on Solar-Energy, Exchange Rate, and Wind datasets within the domain of time series prediction.

Keywords: Fashion trend prediction, time series prediction, Graph neural network