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Autoregression as a means of assessing the strength of seasonality in a time series

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Autoregression as a means of assessing the strength of seasonality in a time series (English)

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author name string

Rahim Moineddin

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1

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Ross EG Upshur

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2

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Eric Crighton

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3

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Muhammad Mamdani

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4

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publication date

15 December 2003

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published in

Population Health Metrics

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volume

1

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issue

1

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page(s)

10

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cites work

A population based time series analysis of asthma hospitalisations in Ontario, Canada: 1988 to 2000

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PubMed Central

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retrieved

7 April 2017

Statistical analysis of the seasonal variation in demographic data

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PubMed Central

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retrieved

28 September 2017

Seasonality comparisons among groups using incidence data

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PubMed Central

reference URL

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=317382

retrieved

28 September 2017

Seasonal variation in childhood asthma hospitalisations in Finland, 1972-1992.

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PubMed Central

reference URL

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retrieved

30 May 2018

Identification of aperiodic seasonality in non-Gaussian time series.

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Crossref

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https://api.crossref.org/works/10.1186%2F1478-7954-1-10

retrieved

21 January 2018

Seasonal variation in asthma hospitalizations and death rates in New Zealand

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Crossref

reference URL

https://api.crossref.org/works/10.1186%2F1478-7954-1-10

retrieved

21 January 2018

Seasonal Variation in Hospital Discharge Diagnosis of Atrial Fibrillation: A Population-Based Study

1 reference

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Crossref

reference URL

https://api.crossref.org/works/10.1186%2F1478-7954-1-10

retrieved

21 January 2018

The recognition and estimation of cyclic trends

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PubMed

reference URL

https://pubmed.ncbi.nlm.nih.gov/14675482

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12 December 2020

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10.1186/1478-7954-1-10

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9 September 2019

https://scigraph.springernature.com/pub.10.1186/1478-7954-1-10

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release_c2e427nxczf4romrv7k4rthr4u

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24 November 2022

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PMC publication ID

317382

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PubMed publication ID

14675482

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ResearchGate publication ID

5636520

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(Q24806867)

Autoregression as a means of assessing the strength of seasonality in a time series

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