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Anomaly detection in streaming time series data with online learning using Amazon Managed Service for Apache Flink

Amazon Web Services

In this post, we demonstrate how to build a robust real-time anomaly detection solution for streaming time series data using Amazon Managed Service for Apache...
2 months ago

Machine learning-based real-time anomaly detection using data pre-processing in the telemetry of server farms

Fast and accurate anomaly detection is critical in telemetry systems because it helps operators take appropriate actions in response to abnormal behaviours.
1 month ago

MIT researchers use large language models to flag problems in complex systems

MIT researchers used large language models to efficiently detect anomalies in time-series data, without the need for costly and cumbersome training steps.
2 months ago

LLMs Transform Anomaly Detection in Wind Energy

An innovative framework called “SigLLM” that uses large language models (LLMs) to detect anomalies in time-series data, specifically identifying faulty wind...
2 months ago

Hands-on Time Series Anomaly Detection using Autoencoders, with Python

Towards Data Science

Anomalous time series are a very serious business. If you think about earthquakes, anomalies are the irregular seismic signals of sudden spikes or drops in...
2 months ago

Researchers from MIT and Harvard Developed UNITS: A Unified Machine Learning Model for Time Series Analysis that Supports a Universal Task Specification Across Various Tasks

Time series analysis is critical in finance, healthcare, and environmental monitoring. This area faces a substantial challenge: the heterogeneity of time...
7 months ago

Novel algorithm for discovering anomalies in data outperforms current software

An algorithm developed by Washington State University researchers can better find data anomalies than current anomaly-detection software, including in...
3 months ago

LLMs can’t outperform a technique from the 70s, but they’re still worth using — here’s why

LLM-based techniques have yet to perform as well as the state-of-the-art deep learning models, or (for 7 datasets) the ARIMA model from the 1970s.
1 month ago

Deep learning anomaly detection in AI-powered intelligent power distribution systems

This study developed a Transformer-GAN model that combines Transformer architectures with GAN technology, efficiently processing complex data and enhancing...
7 months ago

An Efficient Anomaly Detection Method for Industrial Control Systems: Deep Convolutional Autoencoding Transformer Network - Shang - 2024 - International Journal of Intelligent Systems

Wiley Online Library

Our study introduces the CAE-T, a deep convolutional autoencoding transformer network designed for efficient anomaly detection and real-time fault monitoring...
5 months ago