Anomaly detection using an ensemble of models

A system comprising:

• one or more processors;
• and
• one or more computer-readable storage media storing program instructions which, when executed by the one or more processors, are configured to cause the one or more processors to perform a method comprising:
• training an ensemble of deep learning models using clustered time series training data from numerous components in an Information Technology (IT) infrastructure;
• inputting aggregated time series data to the ensemble of deep learning models;
• identifying anomalies in the aggregated time series data based on respective portions of the aggregated time series data that are indicated as anomalous by a majority of deep learning models in the ensemble of deep learning models;
• grouping the anomalies according to relationships between the anomalies;
• and
• performing a mitigation action in response to grouping the anomalies.

A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising instructions configured to cause one or more processors to perform a method comprising:

• training an ensemble of deep learning models using clustered time series training data from numerous components in an Information Technology (IT) infrastructure;
• inputting aggregated time series data to the ensemble of deep learning models;
• identifying anomalies in the aggregated time series data based on respective portions of the aggregated time series data that are indicated as anomalous by a majority of deep learning models in the ensemble of deep learning models;
• grouping the anomalies according to relationships between the anomalies;
• and
• performing a mitigation action in response to grouping the anomalies.