Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting
This work addresses the need for better LLM performance in domain-specific forecasting tasks like energy, finance, and healthcare, though it appears incremental as it builds on existing LLM capabilities with added knowledge infusion.
The paper tackled the problem of enhancing LLMs for time series forecasting by proposing a cross-domain knowledge transfer framework that infuses structured temporal information, resulting in significantly improved predictive accuracy and generalization compared to an uninformed baseline.
With the widespread adoption of Large Language Models (LLMs), there is a growing need to establish best practices for leveraging their capabilities beyond traditional natural language tasks. In this paper, a novel cross-domain knowledge transfer framework is proposed to enhance the performance of LLMs in time series forecasting -- a task of increasing relevance in fields such as energy systems, finance, and healthcare. The approach systematically infuses LLMs with structured temporal information to improve their forecasting accuracy. This study evaluates the proposed method on a real-world time series dataset and compares it to a naive baseline where the LLM receives no auxiliary information. Results show that knowledge-informed forecasting significantly outperforms the uninformed baseline in terms of predictive accuracy and generalization. These findings highlight the potential of knowledge transfer strategies to bridge the gap between LLMs and domain-specific forecasting tasks.