Scalable Multivariate Time-Series Models for Climate Informatics

Author

Yan Liu

Author_Institution

Univ. of Southern California, Los Angeles, CA, USA

Volume

17

Issue

6

fYear

2015

Firstpage

19

Lastpage

26

Abstract

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.

Keywords

climatology; data analysis; geophysics computing; time series; climate change attribution; climate data; climate informatics; data analysis; extreme value time-series analysis; multivariate time-series models; spatiotemporal analysis; Analytical models; Computational modeling; Data models; Hidden Markov models; Meteorology; Spatiotemporal phenomena; Tensile stress; data mining; knowledge management applications; machine learning; scientific computing; time series analysis;

fLanguage

English

Journal_Title

Computing in Science Engineering

Publisher

ieee

ISSN

1521-9615

Type

jour

DOI

10.1109/MCSE.2015.126

Filename

7310915