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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo propomos um algoritmo de correspondência de subsequências que suporta transformadas de média móvel de ordem arbitrária em bancos de dados de séries temporais. A transformação da média móvel reduz o efeito do ruído e tem sido usada em muitas áreas, como a econometria, uma vez que é útil para encontrar tendências gerais. O algoritmo proposto estende o algoritmo de correspondência de subsequências existente proposto por Faloutsos et al. (SUB94 em resumo). Se aplicássemos o algoritmo sem qualquer extensão, teríamos que gerar um índice para cada pedido de média móvel e teríamos sérios custos de armazenamento e tempo de CPU. Neste artigo abordamos o problema usando a noção de interpolação de índice. Interpolação de índice é definido como um método de busca que utiliza um ou mais índices gerados para alguns casos selecionados e realiza busca em todos os casos que satisfazem alguns critérios. O algoritmo proposto, que é baseado na interpolação de índices, pode usar apenas um índice para uma ordem de média móvel pré-selecionada. k e realiza correspondência de subsequência para ordem arbitrária m (
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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
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@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},}
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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 -