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	<Article> 

	<Journal> 

	<PublisherName>International Science Community Association</PublisherName>

	<JournalTitle>Research Journal of Mathematical and Statestical Sciences</JournalTitle> 

	<Issn>2320 - 6047</Issn>

	<Volume>10</Volume>

	<Issue>2</Issue>

	<PubDate PubStatus="ppublish"> 

	<Year>2022</Year> 

	<Month>10</Month> 

	<Day>12</Day> 

	</PubDate>

	</Journal>



	<ArticleTitle>Comparing SARIMA and Adjusted SARIMA models using output based criterion: A case study of Warri monthly rainfall</ArticleTitle> 


	<FirstPage>19</FirstPage>

	<LastPage>28</LastPage>



	<ELocationID EIdType="pii"></ELocationID>

	<Language>EN</Language> 
	<AuthorList>

	
		<Author> 

		<FirstName>Pankratov </FirstName>

		<MiddleName> </MiddleName>

		<LastName>E.L. </LastName>

		<Suffix>1</Suffix>

		<Affiliation>Nizhny Novgorod State University, 23 Gagarin Avenue, Nizhny Novgorod, 603950, Russia</Affiliation>

		</Author>
		<Author> 

		<FirstName>Gabriel </FirstName>

		<MiddleName> </MiddleName>

		<LastName>Amaefula Chibuzo </LastName>

		<Suffix>1</Suffix>

		<Affiliation>Department of Mathematics and Statistics, Federal University Otuoke, Bayelsa State, Nigeria</Affiliation>

		</Author>

	<Author>

	<CollectiveName></CollectiveName>>

	</Author>

	</AuthorList>


	<PublicationType>Case Study</PublicationType>


	<History>  
	<PubDate PubStatus="received">
	<Year>2021</Year>
	<Month>2</Month>
	<Day>28</Day>
	</PubDate>
	<PubDate PubStatus="accepted">										
	<Year>2022</Year> 
	<Month>10</Month>									
	<Day>12</Day> 
	</PubDate>

	</History>
	<Abstract>The paper models Warri monthly rainfall (WMRF) pattern by comparing SARIMA and Adjusted SARIMA (ASARIMA) models using output based criterion. The rainfall data set used covered the period of 1981M1-2016M12. The ADF unit root tests showed that rainfall data is integrated order zero (I(0)). But ACF and PACF exhibit need for seasonal differencing as there are seasonal effects with periodic peaks at lag 12 and 24. The SSDFC was used to compare thirty one models: 17 possible SARIMA (p, d, q) x (P, D, Q)12 models with 14 possible ASARIMA (P, D, Q)12 models. Result indicates that ASARIMA (1,1,3)12 is most appropriate. The diagnostic tests indicate adequacy of the fitted model. Hence, the fitted model is recommended for forecasting WMRF pattern and creating short-term warning against flood in the state.</Abstract>

	<CopyrightInformation>Copyright@ International Science Community Association</CopyrightInformation>

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