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Tags: anomaly detection* + machine learning* + root cause analysis*

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1. MIT Researchers Use Large Language Models to Flag Problems in Complex Systems

   MIT researchers have developed a method using large language models to detect anomalies in complex systems without the need for training. The approach, called SigLLM, converts time-series data into text-based inputs for the language model to process. Two anomaly detection approaches, Prompter and Detector, were developed and showed promising results in initial tests.

   2024-08-14 Tags: llm, anomaly detection, time-series, mit, machine learning, diagnosis, root cause analysis, cybernetics by klotz
2. Determining causality in correlated time series - Amazon Science

   2021-08-20 Tags: time series, anomaly detection, correlation, causality, root cause analysis, amazon, aws, machine learning by klotz
3. GitHub - 7fantasysz/MSCRED: Multi-Scale Convolutional Recurrent Encoder-Decoder

   2020-02-19 Tags: anomaly detection, tensorflow, deep learning, neural network, unsupervised, root cause analysis, papers, github, python by klotz
4. A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

   2020-02-19 Tags: anomaly detection, tensorflow, deep learning, neural network, unsupervised, root cause analysis, papers by klotz

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