Linear Detrending Subsequence Matching in Time-Series Databases
Summary :
Every time-series has its own linear trend, the directionality of a time-series, and removing the linear trend is crucial to get more intuitive matching results. Supporting the linear detrending in subsequence matching is a challenging problem due to the huge number of all possible subsequences. In this paper we define this problem as the linear detrending subsequence matching and propose its efficient index-based solution. To this end, we first present a notion of LD-windows (LD means linear detrending). Using the LD-windows we then present a lower bounding theorem for the index-based matching solution and show its correctness. We next propose the index building and subsequence matching algorithms. We finally show the superiority of the index-based solution.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.4 pp.917-920
- Publication Date
- 2011/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.917
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Keyword
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Myeong-Seon GIL, Yang-Sae MOON, Bum-Soo KIM, "Linear Detrending Subsequence Matching in Time-Series Databases" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 4, pp. 917-920, April 2011, doi: 10.1587/transinf.E94.D.917.
Abstract: Every time-series has its own linear trend, the directionality of a time-series, and removing the linear trend is crucial to get more intuitive matching results. Supporting the linear detrending in subsequence matching is a challenging problem due to the huge number of all possible subsequences. In this paper we define this problem as the linear detrending subsequence matching and propose its efficient index-based solution. To this end, we first present a notion of LD-windows (LD means linear detrending). Using the LD-windows we then present a lower bounding theorem for the index-based matching solution and show its correctness. We next propose the index building and subsequence matching algorithms. We finally show the superiority of the index-based solution.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.917/_p
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@ARTICLE{e94-d_4_917,
author={Myeong-Seon GIL, Yang-Sae MOON, Bum-Soo KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Linear Detrending Subsequence Matching in Time-Series Databases},
year={2011},
volume={E94-D},
number={4},
pages={917-920},
abstract={Every time-series has its own linear trend, the directionality of a time-series, and removing the linear trend is crucial to get more intuitive matching results. Supporting the linear detrending in subsequence matching is a challenging problem due to the huge number of all possible subsequences. In this paper we define this problem as the linear detrending subsequence matching and propose its efficient index-based solution. To this end, we first present a notion of LD-windows (LD means linear detrending). Using the LD-windows we then present a lower bounding theorem for the index-based matching solution and show its correctness. We next propose the index building and subsequence matching algorithms. We finally show the superiority of the index-based solution.},
keywords={},
doi={10.1587/transinf.E94.D.917},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Linear Detrending Subsequence Matching in Time-Series Databases
T2 - IEICE TRANSACTIONS on Information
SP - 917
EP - 920
AU - Myeong-Seon GIL
AU - Yang-Sae MOON
AU - Bum-Soo KIM
PY - 2011
DO - 10.1587/transinf.E94.D.917
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2011
AB - Every time-series has its own linear trend, the directionality of a time-series, and removing the linear trend is crucial to get more intuitive matching results. Supporting the linear detrending in subsequence matching is a challenging problem due to the huge number of all possible subsequences. In this paper we define this problem as the linear detrending subsequence matching and propose its efficient index-based solution. To this end, we first present a notion of LD-windows (LD means linear detrending). Using the LD-windows we then present a lower bounding theorem for the index-based matching solution and show its correctness. We next propose the index building and subsequence matching algorithms. We finally show the superiority of the index-based solution.
ER -
English
