Ciena Corporation

Minming Ni

David Côté

Cengiz Alaettinoglu

William Kaufmann

Saurabh Dinesh Brahmankar

Mark Gibson

Phillip Doelling

Systems and methods for forecasting recurring anomalies in time series data include determining forecast data from historical data of time series data where the forecast data is for a desired future time span; combining the forecast data with the historical data to form a combined time series; determining a normal operating range using the combined time series; detecting anomalies in the forecast data based on the normal operating range; and providing an output including the detected anomalies in the forecast data.

Forecasting recurring anomalies in time series data

Systems and methods for discovering the connectivity topology of cross-layer links in a multi-layer network are provided. In one implementation, a method includes a step of obtaining input data related to a plurality of Network Elements (NEs) operating in a plurality of layers within a multi-layer network having one or more cross-layer port-to-port connectivity links therebetween. The method also includes the step of utilizing Machine Learning (ML) processes and the input data to discover the one or more cross-layer port-to-port connectivity links between pairs of NEs operating in different layers of the plurality of layers within the network.

Discovery of port-to-port connectivity across network layers using machine learning

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Ottawa, Canada

Minming Ni