Embodiments for identifying significant events for finding a root cause of an anomaly collecting time series data for events for each network device by detecting an anomaly in the time series data comprising an outlier on an edge of the time series data by comparing a predicted value of the event to an actual value of the event using a selected forecasting model; declaring the event to be an anomaly at a particular time if a difference between the predicted value and actual value exceed a defined threshold based on residual values for other devices; analyzing in a combined RNN/LSTM process all events for all devices of the network within a time proximity of the particular time of the anomaly to filter usual events and rank each event relative to the anomaly; and displaying a labeled chart of the time series data showing the anomaly in a graph relative to all the events.
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