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On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration (2002)
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| Venue: | SIGKDD'02 |
| Citations: | 325 - 59 self |
BibTeX
@INPROCEEDINGS{Keogh02onthe,
author = {Eamonn Keogh and Shruti Kasetty},
title = {On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration},
booktitle = {SIGKDD'02},
year = {2002},
pages = {102--111},
publisher = {}
}
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Abstract
... mining time series data. Literally hundreds of papers have introduced new algorithms to index, classify, cluster and segment time series. In this work we make the following claim. Much of this work has very little utility because the contribution made (speed in the case of indexing, accuracy in the case of classification and clustering, model accuracy in the case of segmentation) offer an amount of "improvement" that would have been completely dwarfed by the variance that would have been observed by testing on many real world datasets, or the variance that would have been observed by changing minor (unstated) implementation details. To illustrate our point
