A computer-implemented method for automatically detecting and correlating anomalous time-series data related to performance characteristics of a computer network, the method comprising: receiving a plurality of time-series data associated with one or more network data streams; processing the received plurality of time-series data and generating one or more sets of aligned time-series data; detecting anomalous time-stamped data points in each of the one or more sets of aligned time-series data and generating one or more groups of annotated time-series data, wherein the annotation identifies one or more specific time-stamped data points as anomalous; determining a number of anomalous groups of annotated time-series data within all of the one or more groups of annotated time-series data; determining the probability that one or more anomalous groups of annotated time-series data belong to at least one of the groups of annotated time-series data using a generative statistical model and outputting one or more correlated anomalous groups, wherein the one or more anomalous groups are determined to be correlated based on the anomalous group including one or more statistically correlated anomalous time-stamped data points; processing each of the one or more outputted correlated anomalous groups to generate a detailed statistical report for each correlated anomalous group; and outputting to users an aggregated statistical report for the one or more correlated anomalous groups, including probabilities of single and grouped network entity failures for detecting and correlating network failure anomalies and for performing network planning by identifying and modeling critical failure modes and optimizing network resources. receiving a plurality of time-series data associated with one or more network data streams; processing the received plurality of time-series data and generating one or more sets of aligned time-series data; detecting anomalous time-stamped data points in each of the one or more sets of aligned time-series data and generating one or more groups of annotated time-series data, wherein the annotation identifies one or more specific time-stamped data points as anomalous; determining a number of anomalous groups of annotated time-series data within all of the one or more groups of annotated time-series data; determining the probability that one or more anomalous groups of annotated time-series data belong to at least one of the groups of annotated time-series data using a generative statistical model and outputting one or more correlated anomalous groups, wherein the one or more anomalous groups are determined to be correlated based on the anomalous group including one or more statistically correlated anomalous time-stamped data points; processing each of the one or more outputted correlated anomalous groups to generate a detailed statistical report for each correlated anomalous group; and outputting to users an aggregated statistical report for the one or more correlated anomalous groups, including probabilities of single and grouped network entity failures for detecting and correlating network failure anomalies and for performing network planning by identifying and modeling critical failure modes and optimizing network resources. <EOS>