Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry

Name: Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry
Uploaded: 2026-10-18T20:18:21.482150+00:00
Duration: 269 s
Description: The study tested four deep learning models—LSTM/GRU, Attention Transformer, TimesNet, and Autoformer—on IBM Cloud data using the Numenta Anomaly Benchmark (NAB). Results show that short-term deviations dominate real cloud anomalies. TimesNet performed best due to early detection sensitivity, while the improved GRU offered a stable false-alarm profile.

The study tested four deep learning models—LSTM/GRU, Attention Transformer, TimesNet, and Autoformer—on IBM Cloud data using the Numenta Anomaly Benchmark (NAB). Results show that short-term deviations dominate real cloud anomalies. TimesNet performed best due to early detection sensitivity, while the improved GRU offered a stable false-alarm profile.

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