A similarity search method of time series data with combination of Fourier and wavelet transforms

2003 ◽  
Author(s):  
K. Kawagoe ◽  
T. Ueda
Keyword(s):  
Time Series ◽  
Wavelet Transforms ◽  
Similarity Search ◽  
Time Series Data ◽  
Search Method ◽  
Series Data

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 Time Series Classification with Discrete Wavelet Transformed Data

2016 ◽  
Vol 26 (09n10) ◽  
pp. 1361-1377 ◽  
Author(s):  
Daoyuan Li ◽  
Tegawende F. Bissyande ◽  
Jacques Klein ◽  
Yves Le Traon
Keyword(s):  
Time Series ◽  
Classification Accuracy ◽  
Wavelet Transforms ◽  
Time Series Data ◽  
Knowledge Engineering ◽  
Series Data ◽  
Discrete Wavelet ◽  
Time Series Classification ◽  
Time Series Mining ◽  
Compressed Data

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This extended empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in smoothing and reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification accuracy, and that residual details left by popular wavelet compression techniques can sometimes even help to achieve higher classification accuracy than the raw time series data, as they better capture essential local features.

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