LLM Context Window Limitations Make Time-Series Forecasting Inefficient

Feeding large time-series datasets directly into LLM prompts is costly and unreliable due to context window constraints, causing models to hallucinate forecasts rather than apply appropriate statistical methods. Data scientists and ML engineers who want to leverage LLMs for time-series work face a mismatch between how LLMs process information and the volume/structure of temporal data. This is a real architectural friction point, but currently only surfaces as a personal project showcase with minimal external validation of the pain.