Abstract
According to the weakness of traditional methods on outlier mode mining of time series, outlier mode mining is considered as an optimization segmentation problem by using fractal theory, based on the defining fractal outlier, from the viewpoint of outlier affecting orderliness of data set of time series. G-P (Grassberger-Procaccia) algorithm is used to calculate multi-fractal and general dimension. A greedy algorithm named FT-Greedy is designed to solve the optimization problems of outlier mode mining of time series. Then, FT-Greedy is used to detect the exceptional situation in climate time series data. The experiment on shows that the method is feasible to solve the problems on outlier mode mining of climate time series data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zheng, B.X., Xi, Y.G., Du, X.H.: Outlier Mining for Time Series Data Based on Outlier Index. Acta Automatica Sinica 30(1), 70–75 (2004)
Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Using Ideas from Immunology. In: 5th International Conference on Intelligent Systems, pp. 82–87 (1999)
Shahabi, C., Tian, X., Zhao, W.: TSA-tree: A Wavelet-based Approach to Improve the Efficiency of Multi-level Surprise and Trend Queries. In: 12th International Conference on Scientific and Statistical Database Management, pp. 55–68. IEEE Press, Banff (2000)
Keogh, E., Lonardi, S., Chiu, B.: Finding Surprising Patterns in a Time Series Database in Linear Time and Space. In: 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, pp. 550–556 (2002)
Ma, J., Perkins, S.: Time-Series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks (2003)
Xue, A.R., He, W.H.: New Segment Method Based on Outlier Mining for Time Series Data. Computer Engineering and Design 28(7), 4875–4877 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hu, J., Sun, JH. (2011). Outlier Mode Mining in Climate Time Series Data with Fractal Theory. In: Yu, Y., Yu, Z., Zhao, J. (eds) Computer Science for Environmental Engineering and EcoInformatics. CSEEE 2011. Communications in Computer and Information Science, vol 158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22694-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-22694-6_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22693-9
Online ISBN: 978-3-642-22694-6
eBook Packages: Computer ScienceComputer Science (R0)