The recent advancements in the field of time series forecasting and fashion product performance prediction have seen a significant shift towards leveraging diffusion models and graph neural networks. These models, which are inherently capable of handling continuous-time processes and capturing long-range dependencies, are proving to be highly effective in addressing the challenges posed by domain shifts and the lack of historical data. Notably, the integration of multimodal data with diffusion models has led to the development of novel pipelines that offer state-of-the-art accuracy in forecasting tasks, particularly in the fast-fashion industry. Additionally, innovations in temporal integration schemes and adaptive multi-step rollout strategies are enhancing the robustness and accuracy of spatio-temporal auto-regressive predictions, mitigating error accumulation over long-term forecasts. These developments collectively indicate a move towards more versatile and adaptive forecasting frameworks that can operate effectively under practical constraints such as limited data and minimal model capacity.

Among the noteworthy contributions, the introduction of Dif4FF and MDiFF showcases the power of multimodal diffusion models in fashion forecasting, achieving new benchmarks in accuracy and efficiency. The Adams-Bashforth time integration with adaptive multi-step rollout strategy stands out for its significant improvements in long-term prediction robustness and adaptability in challenging scenarios.