# Proactive Network Maintenance using Fast, Accurate Anomaly Localization and Classification on 1-D Data Series

arXiv: https://arxiv.org/abs/2007.08752 · HTML: https://mlsanity.com/paper/2007.08752/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Identifying the root cause of cable network problems with machine learning](https://mlsanity.com/paper/2203.06989/) (2022)

## Abstract (arXiv)

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.