Spatio-temporal outlier detection in precipitation data

Elizabeth Wu, Wei Liu, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

The detection of outliers from spatio-temporal data is an important task due to the increasing amount of spatio-temporal data available and the need to understand and interpret it. Due to the limitations of current data mining techniques, new techniques to handle this data need to be developed. We propose a spatio-temporal outlier detection algorithm called Outstretch, which discovers the outlier movement patterns of the top-k spatial outliers over several time periods. The top-k spatial outliers are found using the Exact-Grid Top- k and Approx-Grid Top- k algorithms, which are an extension of algorithms developed by Agarwal et al. [1]. Since they use the Kulldorff spatial scan statistic, they are capable of discovering all outliers, unaffected by neighbouring regions that may contain missing values. After generating the outlier sequences, we show one way they can be interpreted, by comparing them to the phases of the El Niño Southern Oscilliation (ENSO) weather phenomenon to provide a meaningful analysis of the results.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages115-133
Number of pages19
Volume5840 LNCS
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2nd International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008 - Las Vegas, NV
Duration: 24 Aug 200827 Aug 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5840 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008
CityLas Vegas, NV
Period24/8/0827/8/08

Fingerprint

Outlier Detection
Outlier
Spatio-temporal Data
Data mining
Statistics
Scan Statistic
Grid
Missing Values
Weather
Data Mining

Keywords

  • Data Mining
  • Outlier Detection
  • Precipitation Extremes
  • South America
  • Spatio-Temporal

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Wu, E., Liu, W., & Chawla, S. (2010). Spatio-temporal outlier detection in precipitation data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5840 LNCS, pp. 115-133). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5840 LNCS). https://doi.org/10.1007/978-3-642-12519-5_7

Spatio-temporal outlier detection in precipitation data. / Wu, Elizabeth; Liu, Wei; Chawla, Sanjay.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5840 LNCS 2010. p. 115-133 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5840 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wu, E, Liu, W & Chawla, S 2010, Spatio-temporal outlier detection in precipitation data. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5840 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5840 LNCS, pp. 115-133, 2nd International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008, Las Vegas, NV, 24/8/08. https://doi.org/10.1007/978-3-642-12519-5_7
Wu E, Liu W, Chawla S. Spatio-temporal outlier detection in precipitation data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5840 LNCS. 2010. p. 115-133. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-12519-5_7
Wu, Elizabeth ; Liu, Wei ; Chawla, Sanjay. / Spatio-temporal outlier detection in precipitation data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5840 LNCS 2010. pp. 115-133 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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