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Industrial processes are monitored by a large number of various sensors thatproduce time-series data. Deep Learning offers a possibility to create anomalydetection methods that can aid in preventing malfunctions and increasingefficiency. But creating such a solution can be a complicated task, withfactors such as inference speed, amount of available data, number of sensors,and many more, influencing the feasibility of such implementation. We introducethe DeTAVIZ interface, which is a web browser based visualization tool forquick exploration and assessment of feasibility of DL based anomaly detectionin a given problem. Provided with a pool of pretrained models and simulationresults, DeTAVIZ allows the user to easily and quickly iterate through multiplepost processing options and compare different models, and allows for manualoptimisation towards a chosen metric.

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