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Discrete Fourier Transform
High Performance
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Time Delay
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High Performance Discovery in Time Series: Techniques and Case Studies
High Performance Discovery in Time Series: Techniques and Case Studies,Yunyue Zhu,Dennis Shasha
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High Performance Discovery in Time Series: Techniques and Case Studies
(
Citations: 15
)
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Yunyue Zhu
,
Dennis Shasha
This paper proposes e#cient methodsfor solving this problem based on Discrete Fourier Transforms and a three level timeinterval hierarchy. Extensive experiments on
synthetic data
and real world financialtrading data show that our algorithm beats the direct computation approach by severalorders of magnitude. It also improves on previous
Fourier Transform
approachesby allowing the e#cient computation of time-delayed correlation over any size slidingwindow and any time delay. Correlation...
Published in 2004.
Cumulative
Annual
Citation Context
(13)
...High-frequency financial data in the stock market are generally characterized as bursty in various time scales; see, for example,
18
25
...
M. Y. Hassan
,
et al.
Modelling Poisson marked point processes using bivariate mixture trans...
...can be borrowed from the literature [19, 22,
26
, 24]...
Mohammad Maifi Hasan Khan
,
et al.
Diagnostic powertracing for sensor node failure analysis
...Shasha et al. proposed in [
20
]a n incremental one-pass algorithm, which can identify bursts over a variety of dynamically chosen window sizes...
Conny Franke
,
et al.
In-Network Detection of Anomaly Regions in Sensor Networks with Obstac...
...This is called elastic burst detection [
9
]...
...Shasha et al. [
9
] propose a technique for elastic burst detection in time series, where bursts can be identified over a variety of dynamically chosen window sizes...
...To identify the applied methods, we abbreviate single exponential smoothing by ses; and double exponential smoothing by des;;;cnt . Determining the applied threshold on the basis of the simple mean, as proposed by [
9
], is identified by . If we apply a dynamic method based on jumping windows we write dyn and indicate the used window size by a succeeding number...
Marcel Karnstedt
,
et al.
Adaptive burst detection in a stream engine
...In Figure 5 a decision tree [
20
] has been depicted...
Mohammad Tamviruzzaman
,
et al.
ePet: when cellular phone learns to recognize its owner
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Citations
(15)
Modelling Poisson marked point processes using bivariate mixture transition distributions
M. Y. Hassan
,
M. Y. El-Bassiouni
Journal:
Journal of Statistical Computation and Simulation - J STAT COMPUT SIM
, vol. ahead-of-p, no. ahead-of-p, pp. 1-13, 2012
Diagnostic powertracing for sensor node failure analysis
(
Citations: 3
)
Mohammad Maifi Hasan Khan
,
Hieu Khac Le
,
Michael LeMay
,
Paria Moinzadeh
,
Lili Wang
,
Yong Yang
,
Dong Kun Noh
,
Tarek F. Abdelzaher
,
Carl A. Gunter
,
Jiawei Han
,
Xin Jin
Conference:
Information Processing in Sensor Networks - IPSN
, pp. 117-128, 2010
In-Network Detection of Anomaly Regions in Sensor Networks with Obstacles
(
Citations: 3
)
Conny Franke
,
Marcel Karnstedt
,
Daniel Klan
,
Michael Gertz
,
Kai-uwe Sattler
,
Wolfram Kattanek
Journal:
Computer Science - Research and Development
, vol. 24, no. 3, pp. 367-386, 2009
Adaptive burst detection in a stream engine
(
Citations: 2
)
Marcel Karnstedt
,
Daniel Klan
,
Christian Pölitz
,
Kai-uwe Sattler
,
Conny Franke
Conference:
ACM Symposium on Applied Computing - SAC
, pp. 1511-1515, 2009
ePet: when cellular phone learns to recognize its owner
(
Citations: 1
)
Mohammad Tamviruzzaman
,
Sheikh Iqbal Ahamed
,
Chowdhury Sharif Hasan
,
Casey O'brien
Published in 2009.