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Streaming Time Series Summarization Using User-Defined Amnesic Functions
July 2008 (vol. 20 no. 7)
pp. 992-1006
ASCII Text
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Themis Palpanas, Michail Vlachos, Eamonn Keogh, Dimitrios Gunopulos, "Streaming Time Series Summarization Using User-Defined Amnesic Functions," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 7, pp. 992-1006, July, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2007.190737,
author = {Themis Palpanas and Michail Vlachos and Eamonn Keogh and Dimitrios Gunopulos},
title = {Streaming Time Series Summarization Using User-Defined Amnesic Functions},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {20},
number = {7},
issn = {1041-4347},
year = {2008},
pages = {992-1006},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2007.190737},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Streaming Time Series Summarization Using User-Defined Amnesic Functions
IS - 7
SN - 1041-4347
SP992
EP1006
EPD - 992-1006
A1 - Themis Palpanas,
A1 - Michail Vlachos,
A1 - Eamonn Keogh,
A1 - Dimitrios Gunopulos,
PY - 2008
KW - time series
KW - amnesic approximation
KW - streaming algorithm
VL - 20
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
The past decade has seen a wealth of research on time series representations. The vast majority of research has concentrated on representations that are calculated in batch mode and represent each value with approximately equal fidelity. However, the increasing deployment of mobile devices and real time sensors has brought home the need for representations that can be incrementally updated, and can approximate the data with fidelity proportional to its age. The latter property allows us to answer queries about the recent past with greater precision, since in many domains recent information is more useful than older information. We call such representations amnesic. While there has been previous work on amnesic representations, the class of amnesic functions possible was dictated by the representation itself. In this work, we introduce a novel representation of time series that can represent arbitrary, user-specified amnesic functions. We propose online algorithms for our representation, and discuss their properties. Finally, we perform an extensive empirical evaluation on 40 datasets, and show that our approach can efficiently maintain a high quality amnesic approximation.

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Index Terms:
time series, amnesic approximation, streaming algorithm
Citation:
Themis Palpanas, Michail Vlachos, Eamonn Keogh, Dimitrios Gunopulos, "Streaming Time Series Summarization Using User-Defined Amnesic Functions," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 7, pp. 992-1006, July 2008, doi:10.1109/TKDE.2007.190737
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