Assumption-Free Anomaly Detection in Time Series

Li Wei      Nitin Kumar      Venkata Lolla      Eamonn Keogh      Stefano Lonardi      Chotirat Ann Ratanamahatana

University of California,  Riverside

Department of Computer Science & Engineering

Riverside, CA 92521, USA

{wli, nkumar, vlolla, eamonn, stelo, ratana}

This web page contains full color examples and dataset of the figures presented in the paper along with many others that follow this work.

  1. Powerpoint Version Download

  2. HTML Version View

Data sets used for experimental evaluation in the paper:

  1. Figure 1. ECG Dataset 1

  2. Figure 3. DNA Data

  3. Figure 6. ECG Dataset 2

  4. Figure 7. ECG Dataset 3

Anomaly Detection Tool

  1. Description: a tool to detect anomalies in time series.

  2. Instructions to use this tool:

  1. Before running the applet, you should

  1. Sample datasets and settings

  1. The anomaly detection applet

No Java Plug-in support for applet, see

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