Scalable Multivariate Time-Series Models for Climate Informatics

被引：48
|
作者：
University of Southern California, Los Angeles, United States [1 ]
机构：
[1] University of Southern California, Los Angeles
来源：
Comput. Sci. Eng. | / 6卷 / 19-26期
关键词：
data mining; knowledge management applications; machine learning; Scientific computing; time series analysis;
D O I：
10.1109/MCSE.2015.126
中图分类号：
学科分类号：
摘要：
The increasing volume of climate data has created the need for scientists to develop scalable data analysis tools beyond traditional techniques. Climate data not only have a massive scale but also high dimension and complex dependency structures, making the analysis task extremely challenging. Climate informatics leverages advanced algorithmic tools from data science to solve problems in climate science. This article showcases how scalable multivariate time-series models can be developed for climate change attribution, spatiotemporal analysis, and extreme value time-series analysis. © 1999-2011 IEEE.
引用
收藏
页码：19 / 26
页数：7
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