Restoration of Missing Time-Series Data via Multiple Sine Functions Decomposition with Guangzhou-Temperature Application
type: RoCrateNanopub
created: 2026-03-03T15:50:25.492+01:00
creator: RoCrateBot
type: Nanopublication
has assertion: assertion
has provenance: provenance
has publication info: pubinfo

sig
has algorithm: RSA
signed by: RoCrateBot

Ecology
type: DefinedTerm

Generation Service
type: Agent
type: Person

IEEE
type: organization

bibliographic reference
type: Concept

CNR-ISMAR
type: organization

restoration of Missing Time-Series Data
type: Expression

mathematics
type: Domain

restored data
type: Expression

Guangzhou
type: Concept

Canton
type: Place

model
type: Concept

application
type: Lemma
norm score: 6.23885918003565
score: 7.0

chemical analysis
type: Concept
norm score: 4.016337644656229
score: 5.9

restoration of Missing Time-Series Data
type: Phrase
norm score: 6.432038834951456
score: 5.3

restoration
type: Concept
norm score: 2.7229407760381212
score: 4.0

oceanography
type: FieldOfResearch
norm score: 50.0
score: 0.4890129268169403

time series
type: Concept
norm score: 3.6759700476514636
score: 5.4

quantitative analysis
type: Concept
norm score: 3.8121170864533696
score: 5.6

msfd model
type: Phrase
norm score: 21.116504854368927
score: 17.4

value
type: Concept
norm score: 6.262763784887678
score: 9.2

multiple sine function decomposition
type: Lemma
norm score: 7.843137254901961
score: 8.8

oceanography
type: FieldOfResearch
norm score: 50.0
score: 0.4890129268169403

Chemistry
type: IPTC
path: Science and technology/Natural science/Chemistry

model
type: Lemma
norm score: 6.951871657754011
score: 7.8

effectiveness
type: Concept
norm score: 2.7229407760381212
score: 4.0

data analysis
type: Lemma
norm score: 7.308377896613189
score: 8.2

https://w3id.org/ro-id/445f93f5-696a-45b4-b4ef-50f92f281e76
type: Sentence
name: In this paper, an algorithmically innovative model termed multiple sine function decomposition (MSFD) model is proposed and developed for restoring the missing data about monthly average temperature (MAT) of Guangzhou, which is a representative major city of China.
norm score: 19.38435940099834
score: 23.3

Chemistry
type: IPTC
path: Science and technology/Natural science/Chemistry

value
type: Concept
norm score: 6.535057862491491
score: 9.6

China
type: Place
wikidata: https://www.wikidata.org/wiki/Q148

data
type: Concept
norm score: 10.279101429543907
score: 15.1

Canton
type: Lemma
norm score: 5.52584670231729
score: 6.2

model
type: Lemma
norm score: 6.684491978609626
score: 7.5

removed data
type: Phrase
norm score: 7.281553398058252
score: 6.0

time-series data
type: Phrase
norm score: 12.62135922330097
score: 10.4

information
type: Concept
norm score: 5.85432266848196
score: 8.6

value
type: Lemma
norm score: 10.606060606060606
score: 11.9

data analysis
type: Lemma
norm score: 7.397504456327987
score: 8.3

data analysis
type: Concept
norm score: 4.560925799863853
score: 6.7

standard function
type: Concept
norm score: 2.314499659632403
score: 3.4

https://w3id.org/ro-id/72353d4c-9bbd-43e5-b50c-bcef51808f21
type: Sentence
name: In this paper, an algorithmically innovative model termed multiple sine function decomposition (MSFD) model is proposed and developed for restoring the missing data about monthly average temperature (MAT) of Guangzhou, which is a representative major city of China.
norm score: 18.219633943427624
score: 21.9

earth sciences
type: FieldOfResearch
norm score: 50.0
score: 0.4890129268169403

restoration
type: Concept
norm score: 2.9952348536419335
score: 4.4

mathematical and computer sciences
type: NASA
norm score: 50.0
score: 0.6513797640800476

msfd model
type: Phrase
norm score: 23.90776699029126
score: 19.7

Canton
type: Concept
norm score: 3.2675289312457454
score: 4.8

computer science
type: Domain
norm score: 55.45454545454545
score: 12.2

representative major city
type: Phrase
norm score: 6.310679611650485
score: 5.2

data
type: Concept
norm score: 12.049012933968685
score: 17.7

removed data
type: Phrase
norm score: 5.461165048543689
score: 4.5

Experimental results indicate that the proposed MSFD model can effectively estimate the intentionally removed data, and the values of the restored data are quite close to the values of the true data.
type: Sentence
norm score: 19.88352745424293
score: 23.9

computer operations and hardware
type: NASA
norm score: 50.0
score: 0.6513797640800476

Guangzhou-temperature application
type: Phrase
norm score: 5.582524271844659
score: 4.6

time series
type: Lemma
norm score: 5.793226381461675
score: 6.5

quantitative analysis
type: Lemma
norm score: 6.0606060606060606
score: 6.8

innovative model
type: Phrase
norm score: 5.339805825242719
score: 4.4

service-account-enrichment
type: Agent

Restoration of Missing Time-Series Data via Multiple Sine Functions Decomposition with Guangzhou-Temperature Application
type: ResearchObject
type: LiveRO
type: Dataset
type: BibliographyResearchObject
performs task: bibliographic reference
about: Ecology
content size: 4559
contributor: Generation Service
copyright holder: IEEE
creator: Generation Service
date created: 2018-06-20 11:32:22.658000+00:00
date modified: 2025-03-05 12:46:24.466169+00:00
date published: 2018-06-20 11:32:22.658000+00:00
description: The restoration of missing data is an important concern for data analysis. In this paper, an algorithmically innovative model termed multiple sine function decomposition (MSFD) model is proposed and developed for restoring the missing data about monthly average temperature (MAT) of Guangzhou, which is a representative major city of China. The proposed MSFD model is formed by successive approximation based on the existing data. After that, the MSFD model with parameters and structure determined is exploited to restore the missing data. Experimental results indicate that the proposed MSFD model can effectively estimate the intentionally removed data, and the values of the restored data are quite close to the values of the true data. In addition, with quantitative and qualitative analysis, the effectiveness of the proposed model is further illustrated.
encoding format: application/ld+json
end time: 2014-01-01
license: no-permission
distribution category: Open access
generated at time: 2014-01-01
domain: computer science
place: China
place: Canton
concept: chemical analysis
concept: restoration
concept: time series
concept: quantitative analysis
concept: value
concept: effectiveness
concept: data
concept: information
concept: data analysis
concept: standard function
concept: Canton
concept: application software
field of research: oceanography
field of research: earth sciences
IPTC: Chemistry
lemma: application
lemma: multiple sine function decomposition
lemma: model
lemma: data analysis
lemma: Canton
lemma: value
lemma: time series
lemma: quantitative analysis
lemma: qualitative analysis
NASA: mathematical and computer sciences
NASA: computer operations and hardware
phrase: restoration of Missing Time-Series Data
phrase: msfd model
phrase: removed data
phrase: time-series data
phrase: representative major city
phrase: Guangzhou-temperature application
phrase: innovative model
sentence: https://w3id.org/ro-id/445f93f5-696a-45b4-b4ef-50f92f281e76

service-account-generation-service
type: Agent

The restoration of missing data is an important concern for data analysis.
type: Sentence
norm score: 11.73044925124792
score: 14.1

computer operations and hardware
type: NASA
norm score: 50.0
score: 0.6513797640800476

innovative model
type: Phrase
norm score: 5.9466019417475735
score: 4.9

chemical analysis
type: Concept
norm score: 2.314499659632403
score: 3.4

qualitative analysis
type: Lemma
norm score: 6.506238859180035
score: 7.3

Canton
type: Place
wikidata: https://www.wikidata.org/wiki/Q16572

multiple sine function decomposition
type: Lemma
norm score: 7.575757575757575
score: 8.5

information
type: Concept
norm score: 5.990469707283867
score: 8.8

data analysis
type: Concept
norm score: 4.560925799863853
score: 6.7

Canton
type: Lemma
norm score: 5.436720142602495
score: 6.1

earth sciences
type: FieldOfResearch
norm score: 50.0
score: 0.4890129268169403

China
type: Place
wikidata: https://www.wikidata.org/wiki/Q148

application software
type: Concept
norm score: 4.084411164057181
score: 6.0

computer science
type: Domain
norm score: 44.545454545454554
score: 9.8

Canton
type: Concept
norm score: 3.2675289312457454
score: 4.8

Experimental results indicate that the proposed MSFD model can effectively estimate the intentionally removed data, and the values of the restored data are quite close to the values of the true data.
type: Sentence
norm score: 19.051580698835277
score: 22.9

The restoration of missing data is an important concern for data analysis.
type: Sentence
norm score: 11.73044925124792
score: 14.1

value
type: Lemma
norm score: 10.071301247771835
score: 11.3

Canton
type: Place
wikidata: https://www.wikidata.org/wiki/Q16572

mathematical and computer sciences
type: NASA
norm score: 50.0
score: 0.6513797640800476