Oracle International Corporation

Neelesh Kumar Shukla

Kenny C Gross

Guang Chao Wang

Nickolas Kavantzas

Matthew T Gerdes

Vijayalakshmi Krishnamurthy

Amit Vaid

Mohan Kamath

Saurabh Thapliyal

Sujay Sarkhel

Vidya Mani

Suresh Kumar Golconda

Someshwar Maroti Kale

The disclosed embodiments provide a system that detects sensor anomalies in a univariate time-series signal. During a surveillance mode, the system receives the univariate time-series signal from a sensor in a monitored system. Next, the system performs a staggered-sampling operation on the univariate time-series signal to produce N sub-sampled time-series signals, wherein the staggered-sampling operation allocates consecutive samples from the univariate time-series signal to the N sub-sampled time-series signals in a round-robin ordering. The system then uses a trained inferential model to generate estimated values for the N sub-sampled time-series signals based on cross-correlations with other sub-sampled time-series signals. Next, the system performs an anomaly detection operation to detect incipient sensor anomalies in the univariate time-series signal based on differences between actual values and the estimated values for the N sub-sampled time-series signals. Whenever an incipient sensor anomaly is detected, the system generates a notification.

Staggered-sampling technique for detecting sensor anomalies in a dynamic univariate time-series signal

Techniques for selecting universal hyper parameters for use in a set of machine learning models across multiple computing environments include detection of a triggering condition for tuning a set of universal hyper parameters. The set of universal hyper parameters dictate configuration of the set of machine learning models that are independently executing, respectively, in the multiple computing environments. Based on the detected triggering condition, a first subset of universal hyper parameters from the set of universal hyper parameters are altered to generate a second set of universal hyper parameters. The second set of universal hyper parameters are applied to the set of machine learning models across the multiple computing environments.

Hyper parameter tuning for machine learning models

Growing community of inventors

Lucknow, India

Neelesh Kumar Shukla