Temporal Data Outlier Detection Task

A Temporal Data Outlier Detection Task is a sequential data outlier detection task that is a temporal analysis task (for temporal data).

• Context:
  • It can range from being a Discrete-Time Outlier Detection Task to being a Continuous-Time Outlier Detection Task.
  • It can range from (typically) being a Within-a-Temporal Sequence Outlier Detection Task to being a Entire-Temporal Sequence Outlier Detection Task.
  • It can be solved by a Temporal Data Outlier Detection System (that implements a Temporal Data Outlier Detection Algorithm).
  • ...
• Example(s):
  • a Fraud Detection Task.
  • a Intrusion Detection Task.
  • a System Failure Detection Task.
  • …
• Counter-Example(s):
  • DNA Gene-Sequence Outlier Detection.
  • a Non-Temporal Spatial Outlier Detection Task.
  • a Temporal Motif Analysis Task.
• See: Spatial Outlier Detection.

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References

2014

• (Gupta et al., 2014a) ⇒ Madhu Gupta, Jing Gao, Charu C Aggarwal, and Jiawei Han. (2014). “Outlier Detection for Temporal Data: A Survey.” In: Knowledge and Data Engineering, IEEE Transactions on, 26(9).

2009

• (Mueen et al., 2009) ⇒ Abdullah Mueen, Eamonn J. Keogh, and Nima Bigdely Shamlo. (2009). “Finding Time Series Motifs in Disk-Resident Data.” In: Proceedings of the Ninth IEEE International Conference on Data Mining (ICDM 2009). doi:10.1109/ICDM.2009.15
  • QUOTE: Time series motifs are sets of very similar subsequences of a long time series.

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