Paper Info
Title
W-Tss: A Wavelet-Based Algorithm For Discovering Time Series Shapelets
Abstract
Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.
Year
DOI
Venue
2021
10.3390/s21175801
SENSORS
Keywords
DocType
Volume
shapelets, wavelets, time series mining, time series classification, pattern discovery
Journal
21
Issue
ISSN
Citations 
17
1424-8220
0
PageRank 
References 
Authors
0.34
0
10
Name
Order
Citations
PageRank
Kenan Li100.34
Huiyu Deng200.34
John Morrison300.34
Rima Habre431.17
Meredith Franklin500.34
Yao-Yi Chiang636031.33
Katherine Sward700.34
Frank D. Gilliland800.34
José Luis Ambite9958110.89
Sandrah P. Eckel1010.76