Abstract: Described are techniques for automated anomaly detection including a technique comprising training an ensemble of deep learning models using clustered time series training data from numerous components in an Information Technology (IT) infrastructure. The technique further comprises inputting aggregated time series data to the ensemble of deep learning models and 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. The technique further comprises grouping the anomalies according to relationships between the anomalies and performing a mitigation action in response to grouping the anomalies.

Abstract: Described are techniques for automated anomaly detection including a technique comprising training an ensemble of deep learning models using clustered time series training data from numerous components in an Information Technology (IT) infrastructure. The technique further comprises inputting aggregated time series data to the ensemble of deep learning models and 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. The technique further comprises grouping the anomalies according to relationships between the anomalies and performing a mitigation action in response to grouping the anomalies.

Abstract: Disclosed is a method and system for processing posts retrieved from social media to improve performance of a supply chain of products and services. The system may collect posts of users from social media and may classify the posts into a plurality of categories. The system may determine an opinion of the users based upon the classified posts. The system may then calculate the latent variables for the supply chain. Further, the system may calculate modified Key Performance Indicators (KPI's) of the supply chain based on existing KPI's of the supply chain, the opinion of the users, and the latent variables. Subsequently, the system may manage supply chain enablers of the products and the services based on the modified KPI's.