Spatio-temporal outlier detection in precipitation data

Elizabeth Wu, Wei Liu, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

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 publicationKnowledge Discovery from Sensor Data - Second International Workshop, Sensor-KDD 2008, Revised Selected Papers
Pages115-133
Number of pages19
DOIs
StatePublished - 2010
Externally publishedYes
Event2nd International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008 - Las Vegas, NV, United States
Duration: Aug 24 2008Aug 27 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5840 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008
Country/TerritoryUnited States
CityLas Vegas, NV
Period08/24/0808/27/08

Keywords

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

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