This research introduces a novel approach that adapts Large Language Models for time-series forecasting, enabling accurate predictions with limited data.

• Data efficiency - Leverages pre-trained LLMs to overcome the traditional challenge of requiring large training datasets
• Cross-domain application - Works effectively across varied domains including economic planning and weather prediction
• Transfer learning breakthrough - Successfully transfers language representation capabilities to numerical time-series data

For medical applications, this approach offers significant potential for patient monitoring systems, disease progression modeling, and health outcome predictions where historical data may be limited but accuracy remains critical.

LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters