NDLI: Anomaly detection and spatio-temporal analysis of global climate system

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Anomaly detection and spatio-temporal analysis of global climate system

Content Provider	ACM Digital Library
Author	Das, Mahashweta Parthasarathy, Srinivasan
Abstract	Knowledge discovery from temporal, spatial and spatio-temporal data is pivotal for understanding and predicting the behavior of Earth's ecosystem model. An important influence leaving its impact on the ecosystem is the global climate system. In this paper, the Earth Science data that we have analyzed consists of daily global air temperature and precipitation measurements, aggregated from heterogeneous sensors for fifty years (1950--1999). The enormous amount of data that is available for analysis requires employment of data mining techniques for discovering interesting patterns, detecting significant changes and extracting meaningful insights from the data. Our work considers the problem of detecting anomalous (abnormal or unexpected) behavior in the global climate system, discovering teleconnection patterns and providing consequential insights to the analysts.
Starting Page	142
Ending Page	150
Page Count	9
File Format	PDF
ISBN	9781605586687
DOI	10.1145/1601966.1601989
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Climate phenomena Distance-based outlier detection Spatio-temporal analysis Data mining Teleconnections Global climate system
Content Type	Text
Resource Type	Article

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