The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cet article, nous proposons un algorithme d'appariement de sous-séquences qui prend en charge la transformation de moyenne mobile d'ordre arbitraire dans les bases de données de séries chronologiques. La transformation de moyenne mobile réduit l'effet du bruit et a été utilisée dans de nombreux domaines tels que l'économétrie, car elle est utile pour trouver les tendances globales. L'algorithme proposé étend l'algorithme d'appariement de sous-séquences existant proposé par Faloutsos et al. (SUB94 en bref). Si nous appliquions l'algorithme sans aucune extension, nous devrions générer un index pour chaque commande de moyenne mobile et cela entraînerait une surcharge importante en termes de stockage et de temps CPU. Dans cet article, nous abordons le problème en utilisant la notion d'interpolation d'index. Interpolation d'index est défini comme une méthode de recherche qui utilise un ou plusieurs index générés pour quelques cas sélectionnés et effectue une recherche de tous les cas satisfaisant certains critères. L'algorithme proposé, basé sur l'interpolation d'indices, ne peut utiliser qu'un seul indice pour un ordre de moyenne mobile présélectionné. k et effectue une correspondance de sous-séquence pour un ordre arbitraire m (
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
Woong-Kee LOH, Sang-Wook KIM, Kyu-Young WHANG, "Index Interpolation: A Subsequence Matching Algorithm Supporting Moving Average Transform of Arbitrary Order in Time-Series Databases" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 1, pp. 76-86, January 2001, doi: .
Abstract: In this paper we propose a subsequence matching algorithm that supports moving average transform of arbitrary order in time-series databases. Moving average transform reduces the effect of noise and has been used in many areas such as econometrics since it is useful in finding the overall trends. The proposed algorithm extends the existing subsequence matching algorithm proposed by Faloutsos et al. (SUB94 in short). If we applied the algorithm without any extension, we would have to generate an index for each moving average order and would have serious storage and CPU time overhead. In this paper we tackle the problem using the notion of index interpolation. Index interpolation is defined as a searching method that uses one or more indexes generated for a few selected cases and performs searching for all the cases satisfying some criteria. The proposed algorithm, which is based on index interpolation, can use only one index for a pre-selected moving average order k and performs subsequence matching for arbitrary order m (
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_1_76/_p
Copier
@ARTICLE{e84-d_1_76,
author={Woong-Kee LOH, Sang-Wook KIM, Kyu-Young WHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Index Interpolation: A Subsequence Matching Algorithm Supporting Moving Average Transform of Arbitrary Order in Time-Series Databases},
year={2001},
volume={E84-D},
number={1},
pages={76-86},
abstract={In this paper we propose a subsequence matching algorithm that supports moving average transform of arbitrary order in time-series databases. Moving average transform reduces the effect of noise and has been used in many areas such as econometrics since it is useful in finding the overall trends. The proposed algorithm extends the existing subsequence matching algorithm proposed by Faloutsos et al. (SUB94 in short). If we applied the algorithm without any extension, we would have to generate an index for each moving average order and would have serious storage and CPU time overhead. In this paper we tackle the problem using the notion of index interpolation. Index interpolation is defined as a searching method that uses one or more indexes generated for a few selected cases and performs searching for all the cases satisfying some criteria. The proposed algorithm, which is based on index interpolation, can use only one index for a pre-selected moving average order k and performs subsequence matching for arbitrary order m (
keywords={},
doi={},
ISSN={},
month={January},}
Copier
TY - JOUR
TI - Index Interpolation: A Subsequence Matching Algorithm Supporting Moving Average Transform of Arbitrary Order in Time-Series Databases
T2 - IEICE TRANSACTIONS on Information
SP - 76
EP - 86
AU - Woong-Kee LOH
AU - Sang-Wook KIM
AU - Kyu-Young WHANG
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2001
AB - In this paper we propose a subsequence matching algorithm that supports moving average transform of arbitrary order in time-series databases. Moving average transform reduces the effect of noise and has been used in many areas such as econometrics since it is useful in finding the overall trends. The proposed algorithm extends the existing subsequence matching algorithm proposed by Faloutsos et al. (SUB94 in short). If we applied the algorithm without any extension, we would have to generate an index for each moving average order and would have serious storage and CPU time overhead. In this paper we tackle the problem using the notion of index interpolation. Index interpolation is defined as a searching method that uses one or more indexes generated for a few selected cases and performs searching for all the cases satisfying some criteria. The proposed algorithm, which is based on index interpolation, can use only one index for a pre-selected moving average order k and performs subsequence matching for arbitrary order m (
ER -