Finding time series discords based on haar transform (2006)
| Venue: | In Proceeding of the 2nd International Conference on Advanced Data Mining and Applications |
| Citations: | 8 - 1 self |
BibTeX
@INPROCEEDINGS{Fu06findingtime,
author = {Ada Wai-chee Fu and Oscar Tat-wing Leung and Eamonn Keogh and Jessica Lin},
title = {Finding time series discords based on haar transform},
booktitle = {In Proceeding of the 2nd International Conference on Advanced Data Mining and Applications},
year = {2006},
pages = {31--41},
publisher = {Springer Verlag}
}
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Abstract
Abstract. The problem of finding anomaly has received much attention recently. However, most of the anomaly detection algorithms depend on an explicit definition of anomaly, which may be impossible to elicit from a domain expert. Using discords as anomaly detectors is useful since less parameter setting is required. Keogh et al proposed an efficient method for solving this problem. However, their algorithm requires users to choose the word size for the compression of subsequences. In this paper, we propose an algorithm which can dynamically determine the word size for compression. Our method is based on some properties of the Haar wavelet transformation. Our experiments show that this method is highly effective. 1
Citations
| 185 | C.A.: Exact indexing of dynamic time warping - Keogh, Ratanamahatana - 2005 |
| 170 | Efficient time series matching by wavelets - Chan, Fu - 1999 |
| 86 | Towards parameter-free data mining - Keogh, Lonardi, et al. - 2005 |
| 78 | Finding surprising patterns in a time series database in linear time and space - Keogh, Lonardi, et al. - 2002 |
| 76 | Novelty detection in time series data using ideas from immunology - Dasgupta, Forrest - 1996 |
| 42 | Making time-series classification more accurate using learned constraints - Ratanamahatana, Keogh |
| 36 | TSA-tree: A wavelet-based approach to improve the efficiency of multilevel surprise and trend queries on time-series data - Shahabi, Tian, et al. - 2000 |
| 34 | The UCR Time Series Data Mining Archive - Keogh, Folias - 2002 |
| 9 | Approximations to magic: Finding unusual medical time series - Lin, Keogh, et al. - 2005 |
