Point-in-time relative outlier detection

Abstract

Techniques for performing point-in-time relative outlier detection are disclosed herein. In some embodiments, an outlier detection system analyzes metric data based on (a) the values of the metric data detected on a computing resource, (b) a relative change between different metric readings and/or (c) an absolute change between different metric readings relative to the point in time. The outlier detection system may predict whether the computing resource is exhibiting anomalous behavior by applying a set of machine-learning (ML) models to the point-in-time values. The ML models allow the outlook detection system to make inferences and adjustments during application runtime rather than relying on static instruction sets to detect and classify outliers. The ML models that are applied may implement unsupervised learning methods that do not rely on pre-training and/or time-series analysis for classification. Thus, the ML models may provide a point-in-time classification without requiring historical metric data to detect outliers.