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Computer Science > Machine Learning

arXiv:2109.10082 (cs)
[Submitted on 21 Sep 2021]

Title:DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series

Authors:Błażej Leporowski, Casper Hansen, Alexandros Iosifidis
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Abstract:Industrial processes are monitored by a large number of various sensors that produce time-series data. Deep Learning offers a possibility to create anomaly detection methods that can aid in preventing malfunctions and increasing efficiency. But creating such a solution can be a complicated task, with factors such as inference speed, amount of available data, number of sensors, and many more, influencing the feasibility of such implementation. We introduce the DeTAVIZ interface, which is a web browser based visualization tool for quick exploration and assessment of feasibility of DL based anomaly detection in a given problem. Provided with a pool of pretrained models and simulation results, DeTAVIZ allows the user to easily and quickly iterate through multiple post processing options and compare different models, and allows for manual optimisation towards a chosen metric.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2109.10082 [cs.LG]
  (or arXiv:2109.10082v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.10082

Submission history

From: Błażej Leporowski Mr [view email]
[v1] Tue, 21 Sep 2021 10:38:26 UTC (391 KB)
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