Scalable Multivariate Time-Series Models for Climate Informatics

被引：49

作者：

Liu, Yan ^{[1
]}

机构：

[1] University of Southern California, Los Angeles

来源：

Computing in Science and Engineering | 2015年 / 17卷 / 06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

页码：19 / 26

页数：7

共 26 条

[11]

Diks C., Panchenko V., Modified hiemstra-Jones test for granger non-Causality, Computing in Economics and Finance, 192, (2004)

[12]

Brovelli A., Et al., Beta oscillations in a large-Scale sensorimotor cortical Network: Directional influences revealed by granger causality, Proc. Nat'l Academy Science, 101, 26, pp. 9849-9854, (2004)

[13]

Lozano A., Et al., Spatial-Temporal causal modeling for climate change attribution, Proc. 15th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 587-596, (2009)

[14]

Faghmous J.H., Kumar V., Spatio-Temporal data mining for climate data: Advances, challenges, and opportunities, Data Mining and Knowledge Discovery for Big Data, 1, pp. 83-116, (2014)

[15]

Cressie N., Shi T., Kang E., Fixed rank filtering for spatiotemporal data, J. Computational and Graphical Statistics, 19, 3, pp. 724-745, (2010)

[16]

Isaaks E., Vastava R., Applied Geostatistics, (2011)

[17]

Bahadori M.T., Yu R., Liu Y., Fast multivariate spatiotemporal analysis via low rank tensor learning, Proc. 28th Ann. Conf. Neural Information Processing Systems, (2014)

[18]

Coles S., An Introduction to Statistical Modeling of Extreme Values, (2001)

[19]

Beirlant J., Et al., Statistics of Extremes: Theory and Applications, (2006)

[20]

Coles S., Tawn J.A., Modeling extremes of areal rainfall process, J. Royal Statistical Soc. B, 58, 2, pp. 329-347, (1996)

← 1 2 3 →