Imagine an AI that could predict the next big thing in fashion. Not just colors or cuts, but entire style trends, gleaned from complex reports filled with industry jargon and visuals. That's the promise of PAE, a cutting-edge algorithm designed to analyze fashion trend forecasts and extract key product attributes. These forecasts, often presented in dense PDF reports combining text and images, are like cryptic messages about the future of fashion. PAE deciphers these reports, transforming raw data into actionable insights. It tackles the challenge of extracting information from various formats, including images, and deciphering complex layouts. The system uses Large Language Models (LLMs) to understand the nuances of language and identify key attributes like color, material, sleeve style, and more. It even extracts hashtags, providing a direct link to trending themes. But PAE doesn't stop at extraction. It goes a step further, matching these extracted attributes to existing product catalogs. This allows retailers to anticipate demand, plan assortments, and even refine product designs based on predicted trends. The potential impact on the fashion industry is huge. Imagine brands being able to proactively stock items that are predicted to be popular, reducing waste and maximizing sales. Or imagine personalized shopping experiences, where customers are presented with items that perfectly match predicted trends based on their preferences. While the technology is still under development, the initial results are promising. PAE has demonstrated high accuracy in extracting attributes from both text and images, outperforming existing methods. The future of fashion forecasting may be here, and it's powered by AI.