<ul><li>A novel cross-domain knowledge transfer framework is proposed to enhance the performance of Large Language Models (LLMs) in time series forecasting.</li><li>The approach systematically infuses LLMs with structured temporal information to improve their forecasting accuracy in fields like energy systems, finance, and healthcare.</li><li>Results from evaluating the proposed method on a real-world time series dataset show that knowledge-informed forecasting significantly outperforms a naive baseline with no auxiliary information.</li><li>These findings demonstrate the potential of knowledge transfer strategies to improve the predictive accuracy and generalization of LLMs in domain-specific forecasting tasks.</li></ul>

Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting

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