Abstract: Analyzing measurement results of a target system. The analysis is performed by receiving a first matrix including first measurement results of the target system; training a matrix decomposition model with the first matrix to obtain a third matrix of normal or stable measurement results and a fourth matrix of anomalous or unstable measurement results; receiving a second matrix including second measurement results of the target system, wherein the second measurement results are later measurement results compared to the first measurement results; selecting from the third matrix a subset that matches with the second matrix; subtracting the selected subset from the second matrix to obtain a fifth matrix; outputting the fifth matrix or information derived from the fifth matrix for the purpose of evaluating performance of the target system.

Abstract: A computer implemented method of monitoring and controlling a target system, such as a communication network or an industrial process. The method includes receiving information about anomalies in operation of the target system detected by an automated anomaly detection mechanism; automatically determining certainty characteristics of the detected anomalies; submitting detected anomalies to expert evaluation in priority order determined based on the certainty characteristics; and adjusting the determination of certainty characteristics of the detected anomalies and/or the automated anomaly detection mechanism based on results of the expert evaluation.

Abstract: A computer implemented method for obtaining an autoencoder model for the purpose of processing metrics of a target system. The method includes obtaining a data set including metrics associated with the target system, the data set being intended for training the autoencoder for processing further metrics of the target system; masking the data set with a predefined mask configured to exclude certain parts of the data set; using the unmasked parts of the data set for training the autoencoder; masking reconstructed data from the autoencoder with the same predefined mask; using reconstruction error of the unmasked parts of the reconstructed data to update parameters of the autoencoder to obtain autoencoder model; using the masked parts of the data set for testing the autoencoder model; and providing the autoencoder model for processing further metrics of the target system.

Abstract: A computer implemented method of analyzing measurement results of a target system, such as an industrial process or communication network. The method includes receiving a data sample with a plurality of variables representing the measurement results, detecting that the data sample is an anomalous sample using an anomaly detection model, processing the data sample by applying an imputation model to selected subsets of variables of the data sample to obtain imputed samples, and applying the anomaly detection model to the imputed samples, determining anomalous variables of the data sample based on results from the processing of the data sample, and outputting the anomalous variables of the data sample for management operations in the target system.

Abstract: A computer implemented method of analyzing measurement results of a target system, such as an industrial process or communication network. The method includes receiving a data sample with a plurality of variables representing the measurement results, detecting that the data sample is an anomalous sample using an anomaly detection model, processing the data sample by applying an imputation model to selected subsets of variables of the data sample to obtain imputed samples, and applying the anomaly detection model to the imputed samples, determining anomalous variables of the data sample based on results from the processing of the data sample, and outputting the anomalous variables of the data sample for management operations in the target system.