Time series forecasting, predicting future values based on historical data, is a cornerstone of various fields, from weather prediction to financial modeling. While powerful deep learning models like transformers have revolutionized this area, they often come with a hefty computational cost. But what if we could combine the efficiency of simpler linear models with the insights of large language models (LLMs)? Researchers have introduced a novel approach called LeMoLE (LLM-Enhanced Mixture of Linear Experts) that does just that. Traditional linear models struggle to capture the complex, non-linear patterns often found in real-world time series data. LeMoLE tackles this limitation by combining multiple linear experts, each specializing in different time horizons, effectively capturing both short-term fluctuations and long-term trends. The real magic happens when the power of LLMs is infused into this mix. LeMoLE utilizes both static and dynamic text prompts – static prompts providing general context about the data (like the source or variable descriptions), and dynamic prompts containing time-dependent information (such as timestamps). A pre-trained LLM processes these prompts, extracting relevant features that enrich the forecasting process. These textual insights are then seamlessly integrated to refine the combined output of the linear experts, leading to more nuanced and accurate predictions. Experiments on diverse datasets like electricity consumption, traffic flow, and temperature readings have shown that LeMoLE not only outperforms traditional linear models but also surpasses existing LLM-based time series forecasting methods in both accuracy and efficiency. Interestingly, the importance of static versus dynamic prompts varies depending on the nature of the data. For data with strong periodic patterns, the static, global context seems more crucial, while dynamic, local information proves more valuable for non-stationary time series. LeMoLE offers a compelling glimpse into the future of time series forecasting. By blending the strengths of linear models and LLMs, it achieves a powerful synergy of speed and accuracy. This approach opens up exciting possibilities for more efficient and insightful predictions, particularly in scenarios with limited data, paving the way for more robust and reliable forecasting across various domains.