GNNctd: A graph neural network based on complicated temporal dependencies modeling for fashion trend prediction

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 leveraging images from the popular photo-sharing platform Flickr 1 and the renowned fashion show website Vogue.2 2 Furthermore, we propose a model called GNNctd to solve the above problems. This model leverages spatial dependency capture module (SDCM) based on a 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) that used to capture global temporal dependencies. Many experiments substantiate that the proposed GNNctd model can achieve more accurate predictions of fashion trends in our constructed dataset and the other three fashion trend datasets. Simultaneously, the GNNctd model achieves a state-of-the-art performance on the Solar-Energy, Exchange Rate, and Wind datasets within the domain of time series prediction.