LLM-Powered Time-Series Analysis

Large Language Models can serve as expert assistants to significantly accelerate advanced time-series analysis. By using structured prompts, you can generate code and guidance for building and tuning complex models like ARIMA, Prophet, and even deep learning LSTMs. These prompts also help create robust validation strategies, such as walk-forward cross-validation, and perform thorough diagnostic checks on model performance. The technique involves feeding a detailed prompt with your data's context into an LLM, which then returns ready-to-use code and expert analysis to streamline your workflow.