IEICE TRANSACTIONS on Information
Fast Normalization-Transformed Subsequence Matching in Time-Series Databases
Summary :
Normalization transform is known to be very useful for finding the overall trend of time-series data since it enables finding sequences with similar fluctuation patterns. Previous subsequence matching methods with normalization transform, however, would incur index overhead both in storage space and in update maintenance since they should build multiple indexes for supporting query sequences of arbitrary length. To solve this problem, we adopt a single-index approach in the normalization-transformed subsequence matching that supports query sequences of arbitrary length. For the single-index approach, we first provide the notion of inclusion-normalization transform by generalizing the original definition of normalization transform. To normalize a window, the inclusion-normalization transform uses the mean and the standard deviation of a subsequence that includes the window while the original transform uses those of the window itself. Next, we formally prove the correctness of the proposed normalization-transformed subsequence matching method that uses the inclusion-normalization transform. We then propose subsequence matching and index-building algorithms to implement the proposed method. Experimental results for real stock data show that our method improves performance by up to 2.5
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.12 pp.2007-2018
- Publication Date
- 2007/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.12.2007
- Type of Manuscript
- PAPER
- Category
- Data Mining
Authors
Keyword
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Yang-Sae MOON, Jinho KIM, "Fast Normalization-Transformed Subsequence Matching in Time-Series Databases" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 12, pp. 2007-2018, December 2007, doi: 10.1093/ietisy/e90-d.12.2007.
Abstract: Normalization transform is known to be very useful for finding the overall trend of time-series data since it enables finding sequences with similar fluctuation patterns. Previous subsequence matching methods with normalization transform, however, would incur index overhead both in storage space and in update maintenance since they should build multiple indexes for supporting query sequences of arbitrary length. To solve this problem, we adopt a single-index approach in the normalization-transformed subsequence matching that supports query sequences of arbitrary length. For the single-index approach, we first provide the notion of inclusion-normalization transform by generalizing the original definition of normalization transform. To normalize a window, the inclusion-normalization transform uses the mean and the standard deviation of a subsequence that includes the window while the original transform uses those of the window itself. Next, we formally prove the correctness of the proposed normalization-transformed subsequence matching method that uses the inclusion-normalization transform. We then propose subsequence matching and index-building algorithms to implement the proposed method. Experimental results for real stock data show that our method improves performance by up to 2.5
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.12.2007/_p
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@ARTICLE{e90-d_12_2007,
author={Yang-Sae MOON, Jinho KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Fast Normalization-Transformed Subsequence Matching in Time-Series Databases},
year={2007},
volume={E90-D},
number={12},
pages={2007-2018},
abstract={Normalization transform is known to be very useful for finding the overall trend of time-series data since it enables finding sequences with similar fluctuation patterns. Previous subsequence matching methods with normalization transform, however, would incur index overhead both in storage space and in update maintenance since they should build multiple indexes for supporting query sequences of arbitrary length. To solve this problem, we adopt a single-index approach in the normalization-transformed subsequence matching that supports query sequences of arbitrary length. For the single-index approach, we first provide the notion of inclusion-normalization transform by generalizing the original definition of normalization transform. To normalize a window, the inclusion-normalization transform uses the mean and the standard deviation of a subsequence that includes the window while the original transform uses those of the window itself. Next, we formally prove the correctness of the proposed normalization-transformed subsequence matching method that uses the inclusion-normalization transform. We then propose subsequence matching and index-building algorithms to implement the proposed method. Experimental results for real stock data show that our method improves performance by up to 2.5
keywords={},
doi={10.1093/ietisy/e90-d.12.2007},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Fast Normalization-Transformed Subsequence Matching in Time-Series Databases
T2 - IEICE TRANSACTIONS on Information
SP - 2007
EP - 2018
AU - Yang-Sae MOON
AU - Jinho KIM
PY - 2007
DO - 10.1093/ietisy/e90-d.12.2007
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2007
AB - Normalization transform is known to be very useful for finding the overall trend of time-series data since it enables finding sequences with similar fluctuation patterns. Previous subsequence matching methods with normalization transform, however, would incur index overhead both in storage space and in update maintenance since they should build multiple indexes for supporting query sequences of arbitrary length. To solve this problem, we adopt a single-index approach in the normalization-transformed subsequence matching that supports query sequences of arbitrary length. For the single-index approach, we first provide the notion of inclusion-normalization transform by generalizing the original definition of normalization transform. To normalize a window, the inclusion-normalization transform uses the mean and the standard deviation of a subsequence that includes the window while the original transform uses those of the window itself. Next, we formally prove the correctness of the proposed normalization-transformed subsequence matching method that uses the inclusion-normalization transform. We then propose subsequence matching and index-building algorithms to implement the proposed method. Experimental results for real stock data show that our method improves performance by up to 2.5
ER -
English
