Telefonaktiebolaget Lm Ericsson (publ)

Razieh Abbasi Ghalehtaki

Roch Glitho

Fetahi Wuhib

Amin Ebrahimzadeh

Embodiments described herein relate to a method and apparatus for detecting and explaining anomalies in data obtained from an environment using an encoder-decoder machine learning model. A state of the environment is represented by a plurality of features, and the machine learning model is trained with a first set of data samples. Each data sample in the first set of data samples comprises values for each of the plurality of features. The method comprises determining a respective first threshold for each of the plurality of features based on respective maximum reconstruction errors for each feature found during training of the encoder-decoder machine learning model; obtaining an anomalous data sample; determining respective reconstruction errors for each feature in the anomalous data sample using the trained encoder-decoder machine learning model; and determining one or more features in the anomalous data sample that are responsible for the anomalous data sample being anomalous responsive to the reconstruction errors associated with the one or more features being greater than or equal to the respective first thresholds.

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Québec, Canada

Razieh Abbasi Ghalehtaki