Context-driven cold-start Web traffic forecasting

Abstract

Cold-start forecasting is critical in dynamic scenarios where early-stage forecasting drives key decisions, such as content prioritization, resource allocation, and demand estimation before observable trends emerge. In this work, we explore the potential of multimodal forecasting techniques for cold-start forecasting and offer insights into designing more scalable and adaptive models. In particular, we address context-driven cold-start web traffic forecasting that includes textual content and historical web traffic of relevant web pages to generate forecasts when no historical data is available for the target new web page. To advance research in this area, we collect, clean, and align a high-dimensional, multimodal web traffic dataset. We adopt a Retrieval-Augmented Generation framework, and propose the use of large language models (LLMs) for this task. Our experiments demonstrate that the LLM-based strategy consistently outperforms the statistical baseline across multiple forecasting horizons. The best-performing LLM-based model reduces WRMSPE by 0.81% and WAPE by 4.5%, compared with other methods. Furthermore, LLM-based feature extraction enhances contextual understanding, leading to greater stability in long-horizon forecasts.