Source: https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7229529255896920064
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.
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