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
Dalam makalah ini kami mencadangkan algoritma pemadanan susulan yang menyokong perubahan purata bergerak bagi susunan arbitrari dalam pangkalan data siri masa. Transformasi purata bergerak mengurangkan kesan hingar dan telah digunakan dalam banyak bidang seperti ekonometrik kerana ia berguna dalam mencari arah aliran keseluruhan. Algoritma yang dicadangkan memanjangkan algoritma pemadanan susulan sedia ada yang dicadangkan oleh Faloutsos et al. (SUB94 ringkasnya). Jika kami menggunakan algoritma tanpa sebarang sambungan, kami perlu menjana indeks untuk setiap pesanan purata bergerak dan akan mempunyai storan yang serius dan overhed masa CPU. Dalam makalah ini kami menangani masalah menggunakan tanggapan interpolasi indeks. Interpolasi indeks ditakrifkan sebagai kaedah carian yang menggunakan satu atau lebih indeks yang dijana untuk beberapa kes terpilih dan melakukan carian untuk semua kes yang memenuhi beberapa kriteria. Algoritma yang dicadangkan, yang berdasarkan interpolasi indeks, boleh menggunakan hanya satu indeks untuk susunan purata bergerak yang telah dipilih sebelumnya k dan melakukan pemadanan seterusnya untuk susunan sewenang-wenangnya m (
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Salinan
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
Salinan
@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},}
Salinan
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 -