Integrating Natural Language Events into Time Series Forecasting through Agentic LLM Orchestration
This thesis investigates whether Large Language Models (LLMs) can meaningfully reason over natural language event context to produces calibrated adjustments to time series forecasts, and under what conditions that reasoning produces reliable signal. We design an agentic forecasting system in which the LLM acts as a strategic orchestrator rather than a direct numerical predictor: all numerical comp
