Sap Se

Robert Meusel

Atreju Florian Tauschinsky

Janick Frasch

Jaakob Kind

Michael Otto

Minji Lee

Jan Patrick Klein

Stefan Kain

Lukas Brinkmann

Axel Kuhle

Oliver Frendo

Methods, systems, and computer-readable storage media for receiving a time-series of data values associated with a plurality of sensors, each sensor generating at least a portion of the time-series of a respective data value, providing a plurality of auto-regression models, each auto-regression model being provided based on a respective first sub-set of the time-series of data values used as input, and a respective second sub-set of the time-series of data values used as training data during a training process, receiving respective data values associated with a time from and generated by each of the plurality of sensors, determining respective predicted values for each of the auto-regression models, and selectively indicating that an anomaly is present in the system based on respective predicted values for each of the auto-regression models, and the respective data values associated with a time.

Multivariate nonlinear autoregression for outlier detection

In some implementations, there is provided a method, which includes transforming, by the recommendation system, a first data set into the principal component analysis domain; rotating, by the recommendation system, the transformed first data first data set into a common axis system; comparing, by the recommendation system, the rotated, transformed first data set to at least one of a plurality of reference data sets having been rotated into the common axis system; and identifying, by the recommendation system, at least one reference data set, the identifying based on the comparing. Related systems, methods, and articles of manufacture are also disclosed.

PCA-based scoring of the similarity of damage patterns of operational assets

Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.

Sensor data anomaly detection

Methods, systems, and computer-readable storage media for ranking anomaly detection algorithms, including operations of receiving a set of unlabeled data from one or more sensors in a plurality of sensors of an internet of things, generating a plurality of data distributions corresponding to the set of unlabeled data by using a plurality of anomaly detection algorithms, and ranking the plurality of anomaly detection algorithms relative to the set of unlabeled data based on a distance between a first quantile and a second quantile of each of the plurality of data distributions.

Comparing unsupervised algorithms for anomaly detection

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Mannheim, Germany

Robert Meusel