Evidence map›Paper›PMID 34502692›Full record

ArticleSensors (Basel, Switzerland)2021

W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets.

Kenan Li, Huiyu Deng, John Morrison, Rima Habre, Meredith Franklin, Yao-Yi Chiang, Katherine Sward, Frank D Gilliland, José Luis Ambite, Sandrah P Eckel

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Kenan LiDepartment of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA.ORCID 0000-0002-4641-6699
Huiyu DengApplied AI and Data Science, City of Hope National Medical Center, Duarte, CA 91010, USA.
John MorrisonDepartment of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA.
Rima HabreDepartment of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA.
Meredith FranklinDepartment of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA.ORCID 0000-0003-3802-8829
Yao-Yi ChiangDepartment of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Katherine SwardDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, USA.
Frank D GillilandDepartment of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA.
José Luis AmbiteDepartment of Computer Science, University of Southern California, Los Angeles, CA 90089, USA.ORCID 0000-0003-0087-080X
Sandrah P EckelDepartment of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA.

Funding

The Los Angeles PRISMS Center: The Biomedical REAl-Time Health Evaluation (BREATHE) PlatformU54EB022002 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BUI, ALEX · 2015 to 2016
$6.1M
Prisms Informatics Platform - Federated Integration ArchitectureU54EB021973 · NIBIB · UNIVERSITY OF UTAH · PI FACELLI, JULIO CESAR, SWARD, KATHERINE ANN · 2015 to 2016
$5.6M
Pediatric Research using Integrated Sensor Monitoring Systems (PRISMS): Data and Software Coordination and Integration Center (DSCIC)U24EB021996 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI AMBITE, JOSE LUIS, ECKEL, SANDRAH PROCTOR · 2015 to 2015
$5.2M
NIBIB NIH HHS U24 EB021996NIBIB NIH HHS U24EB021996NIBIB NIH HHS U54EB021973NIBIB NIH HHS U54EB022002
6 · The paper itself

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.

Indexed as

Air PollutionAlgorithmsHumansMachine LearningResearch Designpattern discoveryshapeletstime series classificationtime series miningwavelets

Identifiers

PMID34502692
PMCPMC8434226

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.