Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree.

Zheng Zou, Ming-Xing Nie, Xing-Sheng Liu, Shi-Jian Liu

Open access · goldAbstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

0.1field-weighted citation impact, top 61% of its field

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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 4 institutions in 1 country.

Zheng ZouCollege of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China.

Ming-Xing NieSchool of Computer Science, University of South China, Hengyang 421001, China.

Xing-Sheng LiuFujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China.

Shi-Jian LiuFujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China.

Fujian Normal University · CNFujian University of Technology · CNMinjiang University · CNUniversity of South China · CN

Funding

Education Department of Hunan Province 21B0424Fujian Provincial Key Laboratory of Information Processing and Intelligent Control MJUKF-IPIC202208Science and Technology Department of Hunan Province 2021JJ30574

6 · The paper itself

Abstract

Dynamic time warping under limited warping path length (LDTW) is a state-of-the-art time series similarity evaluation method. However, it suffers from high space-time complexity, which makes some large-scale series evaluations impossible. In this paper, an alternating matrix with a concise structure is proposed to replace the complex three-dimensional matrix in LDTW and reduce the high complexity. Furthermore, an evolutionary chain tree is proposed to represent the warping paths and ensure an effective retrieval of the optimal one. Experiments using the benchmark platform offered by the University of California-Riverside show that our method uses 1.33% of the space, 82.7% of the time used by LDTW on average, which proves the efficiency of the proposed method.

Indexed as

AlgorithmsBiological Evolutiondynamic time warpingsimilarity evaluationspace-time complexitytime serieswarping path

Identifiers

PMID35890988

DOI10.3390/s22145305

PMCPMC9318603

OpenAlexW4285804135

What OpenQuestion holds

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LicenceCC BY

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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.