  Proactive network maintenance (PNM) is the concept of using data from a
network to identify and locate network faults, many or all of which could
worsen to become service failures. The separation between the network fault and
the service failure affords early detection of problems in the network to allow
PNM to take place. Consequently, PNM is a form of prognostics and health
management (PHM).
  The problem of localizing and classifying anomalies on 1-dimensional data
series has been under research for years. We introduce a new algorithm that
leverages Deep Convolutional Neural Networks to efficiently and accurately
detect anomalies and events on data series, and it reaches 97.82% mean average
precision (mAP) in our evaluation.
