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Li K, Deng H, Morrison J, Habre R, Franklin M, Chiang YY, Sward K, Gilliland FD, Ambite JL, Eckel SP. W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets. SENSORS 2021; 21:s21175801. [PMID: 34502692 PMCID: PMC8434226 DOI: 10.3390/s21175801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 08/24/2021] [Accepted: 08/24/2021] [Indexed: 11/16/2022]
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.
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Affiliation(s)
- Kenan Li
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.M.); (R.H.); (M.F.); (F.D.G.); (S.P.E.)
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089, USA
- Correspondence:
| | - Huiyu Deng
- Applied AI and Data Science, City of Hope National Medical Center, Duarte, CA 91010, USA;
| | - John Morrison
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.M.); (R.H.); (M.F.); (F.D.G.); (S.P.E.)
| | - Rima Habre
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.M.); (R.H.); (M.F.); (F.D.G.); (S.P.E.)
| | - Meredith Franklin
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.M.); (R.H.); (M.F.); (F.D.G.); (S.P.E.)
| | - Yao-Yi Chiang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA;
| | - Katherine Sward
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, USA;
| | - Frank D. Gilliland
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.M.); (R.H.); (M.F.); (F.D.G.); (S.P.E.)
| | - José Luis Ambite
- Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA;
| | - Sandrah P. Eckel
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.M.); (R.H.); (M.F.); (F.D.G.); (S.P.E.)
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