Oracle has an anomaly detection solution for monitoring time-series telemetry signals for dense-sensor IoT prognostic applications. It integrates an advanced prognostic pattern recognition technique called Multivariate State Estimation Technique (MSET) for high-sensitivity prognostic fault monitoring applications in commercial nuclear power and aerospace applications. MSET has since been spun off and met with commercial success for prognostic Machine Learning (ML) applications in a broad range of safety critical applications, including NASA space shuttles, oil-and-gas asset prognostics, and commercial aviation streaming prognostics. MSET proves to possess significant advantages over conventional ML solutions including neural networks, autoassociative kernel regression, and support vector machines. The main advantages include earlier warning of incipient anomalies in complex time-series signatures, and much lower overhead compute cost due to the deterministic mathematical structure of MSET. Both are crucial for dense-sensor avionic IoT prognostics. In addition, Oracle has developed an extensive portfolio of data preprocessing innovations around MSET to solve the common big-data challenges that cause conventional ML algorithms to perform poorly regarding prognostic accuracy (i.e. false/missed alarm probabilities). Oracle's MSET-based prognostic solution helps increase avionic reliability margins and system availability objectives while reducing costly sources of “no fault found” events that have become a significant sparing-logistics issue for many industries including aerospace and avionics. Moreover, by utilizing and correlating information from all on-board telemetry sensors (e.g., distributed pressure, voltage, temperature, current, airflow and hydraulic flow), MSET is able to provide the best possible prediction of failure precursors and onset of small degradation for the electronic components used on aircrafts, benefiting the aviation Prognostics and Health Management (PHM) system.