A Multi-Dimensional Time Series Data Mining Model for Weather Forecast

Kun Liu

doi:10.4028/www.scientific.net/AMR.532-533.1277

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533A Multi-Dimensional Time Series Data Mining Model...

A Multi-Dimensional Time Series Data Mining Model for Weather Forecast

Abstract:

Based on the analysis and actual application under the background of the ground automatic weather stations research material, through selecting dimension, piecewise linear fitting method, the cluster symbol, this thesis proposes the dimensions redundant reduction algorithm, the extremum slope piecewise linear fitting method, a multi-dimensional time series data mining model for the meteorological data, and uses the model to preliminarily mine the rule of rain weather phenomena. Finally, the experimental results show that this model designed in this paper can predict the weather phenomenon great practicality.

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Periodical:

Advanced Materials Research (Volumes 532-533)

Pages:

1277-1281

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.532-533.1277

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Online since:

June 2012

Authors:

Kun Liu

Keywords:

Data Mining (DM), Time Series, Weather Forecast

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References

[1] Eamonn J Keogh, Themis Palpanas, Yictor B. Zordan, Dimitrios GunoPulos Marc Cardle. Indexing Large Human-Motion Databases. VLDB 2004: 780·791.

DOI: 10.1016/b978-012088469-8.50069-3

Google Scholar

[2] Han Jw, Kam Ber M. Data Mining: Concepts and Techniques[MJ, San Fransisco: Morgan Kaufmann, (2006).

Google Scholar

[3] Hyunjin Yoon, Kiyoung Yang, Cyrus Shahabi. Feature Subset Selection and Feature Ranking for Multivariate Time Series[C]. IEEE Trans. Knowl. Data Eng. 2005, 17(9): 1186-1198.

DOI: 10.1109/tkde.2005.144

Google Scholar

[4] C. Kamath,A. Gezahegne,S. Newsam G.M. Rroberts. Salient Points for Tracking Moving Objects in Video. Image and Video Communications and Processing San Jose, CA, United States. January 17, 2005 through January 20, (2005).

DOI: 10.1117/12.587323

Google Scholar

[5] J. Guyon, A. Elisseeff An Introduction to Variable and Feature Selection[J). Machine Learing Research. 2003, (3): 1157-1182.

Google Scholar