GenAI-LLM Time Series Data Anomaly Detection Without Any Training: How to Advance Be. . .

GenAI-LLM Time Series Data Anomaly Detection Without Any Training: How to Advance Beyond State-of-the-Art Deep Learning Models: Advance #Statistical #Probabilistic #Time #Series #Econometrics #Models for #Dynamic #Uncertainty-#Adversarial #Uncertainty-#Quantum #Uncertainty: Elsevier-SSRN: AI-ML-Quant-Cyber-Crypto-Quantum-Risk Computing: SSRN 128 Top-10 R&D Rankings, Top 0.13% SSRN Authors: https://lnkd.in/ec99Zkd "Identifying one #faulty #turbine in a wind farm can involve looking at hundreds of #signals and millions of data points like finding a needle in a haystack. #Engineers often streamline this complex problem using deep-learning models that can detect #anomalies in #measurements taken repeatedly over time by each turbine, known as #timeseries #data." "But with hundreds of wind turbines recording dozens of signals each hour, training a #deeplearning #model to analyze time-series data is costly and cumbersome. This is compounded by the fact that the model may need to be retrained after #deployment, and wind farm operators may lack the necessary #machinelearning expertise." "In a new study, MIT researchers found that #LLM-s hold the potential to be more efficient #anomaly #detectors for time-series data. Importantly, these pretrained models can be deployed right out of the box.": #LargeLanguageModels can be #ZeroShot anomaly detectors for time series? https://lnkd.in/gR4ddFGA : => 'Gruver et al. [2023] posit that LLMs have an inherent #autoregressive feature which allows them to be effective forecasters... The LLM then generated the next #expected #values, treating time series #forecasting as a next-#token #prediction task. ' => Reminds me of "VARMAX* Models of Co-Integrated Time Series for #HighFrequency #Econometrics" I taught #WallStreet #Quants ~15-years ago. More on VARMAX: AIMLExchange.com: https://lnkd.in/gZfA4pna : Wall Street Quant Projects: https://lnkd.in/gtmMr67r . "The researchers developed a framework, called #SigLLM, which includes a component that converts time-series data into text-based inputs an LLM can process. A user can feed these prepared data to the model and ask it to start identifying anomalies. The LLM can also be used to forecast future time-series data points as part of an #anomaly #detection #pipeline." -- New York State: "Join Dr. Yogi Malhotra to get up to speed on Cloud Technology." USAF-AFRL Ventures: "Do Something Epic: Save the World™": We Create the Digital Future™. You Can Too! Let's Show You How! AIMLExchange™: AIMLExchange.com: We Create the Digital Future™ BRINT™: BRINT.com: From Future of Finance™ to Future of Defense™ C4I-Cyber™: C4I-Cyber.com: Because the Future of the World Depends Upon It™ -- AWS Quantum Valley™ Building the Future of AI-Quantum Networks: Global Risk Management Network LLC Silicon Valley's Next Big Thing™: CEO-CTO-CFO Know-Build-Monetize™ Networks: Join The CxO Metaverse™ C4I-Cyber Quantum Valley-SiliconValley Digital Pioneer USAF-USSF Ventures Engineering Sustainability