STELLA: Semantic Abstractions for Time Series Forecasting with LLMs

This research paper introduces STELLA, a novel approach for leveraging Large Language Models (LLMs) in time series forecasting. The use of semantic abstractions could potentially improve the accuracy and interpretability of LLM-based forecasting models.

• •STELLA proposes a new method for time series forecasting using LLMs.
• •The core idea is to employ semantic abstractions to improve LLM performance.
• •This could lead to more accurate and understandable time series predictions.