Speaker
Description
Recent successes with large language models (LLMs) for natural language processing have prompted exploration into their potential for time series prediction using numerical data. Chronos is a recent framework that pre-trains LLM models for predictions on time series data from various domains. The authors show that the framework can lead to successful zero-shot predictions for unseen time series data in several scenarios. In our research, we investigate the application of the Chronos models to analyze the frequency information from electric power grids to assess the effectiveness of time series predictions using this LLM-based framework. Based on our initial results, we fine-tuned the model with specific time series data from power systems to enhance its predictive accuracy.
