Visual Analysis of Time‐Series Similarities for Anomaly Detection in Sensor Networks

We present a system to analyze time‐series data in sensor networks. Our approach supports exploratory tasks for the comparison of univariate, geo‐referenced sensor data, in particular for anomaly detection. We split the recordings into fixed‐length patterns and show them in order to compare them over time and space using two linked views. Apart from geo‐based comparison across sensors we also support different temporal patterns to discover seasonal effects, anomalies and periodicities.

• Publication year

  2014

• Venue

  Computer graphics forum (Print)

• Publication date

  2014-06-01

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1111/cgf.12396
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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