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klotz: time-series data*

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1. AxolRAG

   This research investigates ways to help large language models interpret time-series sensor data by augmenting measurements with statistical summaries, detected patterns, and environmental context. The study evaluates baseline LLMs, fine-tuned models, and retrieval-augmented generation approaches, finding that combining specialized training with contextual information significantly improves grounding, actionability, and pattern recognition while reducing hallucinations.
   * Augmenting time-series data with social and environmental context
   * Comparing RAG frameworks against baseline and fine-tuned LLMs
   * Enhancing the reliability of automated sensor monitoring systems

   2026-06-12 Tags: axolrag, time-series data, large language models, retrieval-augmented generation, sensor interpretation by klotz
2. Researchers use large language models to flag problems in complex systems

   MIT researchers have developed a framework using large language models (LLMs) to efficiently detect anomalies in time-series data from complex systems like wind farms or satellites, potentially flagging problems before they occur.

   2024-08-19 Tags: llm, anomaly detection, time-series data, complex systems, wind turbines, satellites, production engineering by klotz

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