Trend-Based Similarity Search in Time-Series Data

2010 ◽  
Author(s):  
Martin Suntinger ◽  
Hannes Obweger ◽  
Josef Schiefer ◽  
Philip Limbeck ◽  
Günther Raidl
Keyword(s):  
Time Series ◽  
Similarity Search ◽  
Time Series Data ◽  
Series Data

scholarly journals Similarity Search in Multimedia Time Series Data Using Amplitude-Level Features

2008 ◽  
pp. 123-133 ◽  
Author(s):  
Johannes Aßfalg ◽  
Hans-Peter Kriegel ◽  
Peer Kröger ◽  
Peter Kunath ◽  
Alexey Pryakhin ◽  
...  
Keyword(s):  
Time Series ◽  
Similarity Search ◽  
Time Series Data ◽  
Series Data ◽  
Amplitude Level

Similarity search and pattern discovery in hydrological time series data mining

2010 ◽  
Vol 24 (9) ◽  
pp. 1198-1210 ◽  
Author(s):  
Rulin Ouyang ◽  
Liliang Ren ◽  
Weiming Cheng ◽  
Chenghu Zhou
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Similarity Search ◽  
Time Series Data ◽  
Pattern Discovery ◽  
Series Data ◽  
Time Series Data Mining

scholarly journals Efficient Subsequence Search on Streaming Data Based on Time Warping Distance

1970 ◽  
Vol 5 (1) ◽  
pp. 2-8 ◽  
Author(s):  
Sura Rodpongpun ◽  
Vit Niennattrakul ◽  
Chotirat Ann Ratanamahatana
Keyword(s):  
Time Series ◽  
Similarity Search ◽  
Time Series Data ◽  
Distance Measure ◽  
Computational Cost ◽  
Streaming Data ◽  
Series Data ◽  
Search Problem ◽  
Time Warping ◽  
Subsequence Matching

Many algorithms have been proposed to deal with subsequence similarity search problem in time series data stream. Dynamic Time Warping (DTW), which has been accepted as the best distance measure in time series similarity search, has been used in many research works. SPRING and its variance were proposed to solve such problem by mitigating the complexity of DTW. Unfortunately, these algorithms produce meaningless result since no normalization is taken into account before the distance calculation. Recently, GPUs and FPGAs were used in similarity search supporting subsequence normalization to reduce the computation complexity, but it is still far from practical use. In this work, we propose a novel Meaningful Subsequence Matching (MSM) algorithm which produces meaningful result in subsequence matching by considering global constraint, uniform scaling, and normalization. Our method significantly outperforms the existing algorithms in terms of both computational cost and accuracy.

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