Anomaly Detection and Analysis Framework for Terrestrial Observation and Prediction System (TOPS)

Shared by Petr Votava on Jun 16, 2011

Summary

Author(s) :
Petr Votava, Andrew Michaelis, Hirofumi Hashimoto, Ramakrishna Nemani
Abstract

Terrestrial Observation and Prediction System (TOPS) is a flexible modeling software system that integrates ecosystem models with frequent satellite and surface weather observations to produce ecosystem nowcasts (assessments of current conditions) and forecasts useful in natural resources management, public health and disaster management. We have been extending the Terrestrial Observation and Prediction System (TOPS) to include capability for automated anomaly detection and analysis of both on-line (streaming) and off-line data. While there are large numbers of anomaly detection algorithms for multivariate datasets, we are extending this capability beyond the anomaly detection itself and towards an automated analysis that would discover the possible causes of the anomalies. There are often indirect connections between datasets that manifest themselves during occurrence of external events and rather than searching exhaustively throughout all the datasets, our goal is to capture this knowledge and provide it to the system during automated analysis, which results in more efficient processing. Finally, the project goal is to provide a testbed for a number of anomaly detection and data-mining algorithms and packages, so that they can be tested on large volumes of spatio-temporal datasets.

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Files

TOPS-ESTF-11-0015.pdf
ESTF 2011 paper
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