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Easily embed time-series anomaly detection capabilities into your apps to help users identify problems quickly. Anomaly Detector ingests time-series data of all types and selects the best anomaly detection algorithm for your data to ensure high accuracy. Detect spikes, dips, deviations from cyclic patterns, and trend changes through both univariate and multivariate APIs. Customize the service to detect any level of anomaly. Deploy the anomaly detection service where you need it—in the cloud or at the intelligent edge.

Detect problems for virtually any scenario

There are many types of time-series data, and no one algorithm fits them all. Anomaly Detector assesses your time-series data set and automatically selects the best algorithm and the best anomaly detection techniques from the model gallery. Use the service to ensure high accuracy for scenarios including monitoring IoT device traffic, managing fraud, and responding to changing markets.

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With a huge number of installed systems worldwide, Siemens Healthineers is one of the largest manufacturers of X-ray tube assemblies used for medical applications. To detect and respond to anomalies within the production process as early as possible, Siemens Healthineers is developing AI-based solutions for analyzing vast amounts of production data. For this, Siemens Healthineers applied Anomaly Detector which not only uses a state-of-the-art machine learning model architecture, but also provides explanations about the algorithm’s conclusions.

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