IA-RATD: Industry-Aware Retrieval-Augmented Diffusion Models for Stock Price Forecasting

Abstract

Stock price movements are inherently influenced by complex interdependencies among companies within and across industries. To effectively capture these relationships, we propose IA-RATD, an Industry- Aware Retrieval-Augmented Diffusion model for stock price forecasting. IA-RATD extends retrieval-augmented diffusion by incorporating industry level and interstock relationships to guide the denoising process in time series prediction. Specifically, our framework retrieves relevant historical stock sequences not only based on temporal similarity but also by considering structural connections in the market, enabling the model to leverage contextual information from related companies. Experiments on two major S&P 500 stocks, GOOG and AMZN, demonstrate that IA-RATD consistently outperforms baseline diffusion models, achieving up to 17.6% lower MSE and 28.8% lower MAE compared to state-of-the-art baselines. Our findings highlight the importance of integrating market structure awareness into diffusion-based time series models for financial forecasting. The implementation is available at: https://github.com/AppliedAI-Lab/RATD stock

Diffusion, Industry-aware diffusion, Retrieval-augmented generations, Stock price