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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article">
	<front>
		<journal-meta>
			<journal-id journal-id-type="publisher-id">INFORMATICA</journal-id>
			<journal-title-group>
				<journal-title>Informatica</journal-title>
			</journal-title-group>
			<issn pub-type="epub">0868-4952</issn>
			<issn pub-type="ppub">0868-4952</issn>
			<publisher>
				<publisher-name>VU</publisher-name>
			</publisher>
		</journal-meta>
		<article-meta>
			<article-id pub-id-type="publisher-id">INF10207</article-id>
			<article-id pub-id-type="doi">10.3233/INF-1999-10207</article-id>
			<article-categories>
				<subj-group subj-group-type="heading">
					<subject>Research article</subject>
				</subj-group>
			</article-categories>
			<title-group>
				<article-title>Comparison of ARMA and Multilayer Perceptron Based Methods for Economic Time Series Forecasting</article-title>
			</title-group>
			<contrib-group>
				<contrib contrib-type="Author">
					<name>
						<surname>Raudys</surname>
						<given-names>Aistis</given-names>
					</name>
					<xref ref-type="aff" rid="j_INFORMATICA_aff_000"/>
				</contrib>
				<contrib contrib-type="Author">
					<name>
						<surname>Mockus</surname>
						<given-names>Jonas</given-names>
					</name>
					<xref ref-type="aff" rid="j_INFORMATICA_aff_000"/>
				</contrib>
				<aff id="j_INFORMATICA_aff_000">Institute of Mathematics and Informatics, Akademijos 4, 2600 Vilnius, Lithuania. E-mail: giena@takas.lt</aff>
			</contrib-group>
			<pub-date pub-type="epub">
				<day>01</day>
				<month>01</month>
				<year>1999</year>
			</pub-date>
			<volume>10</volume>
			<issue>2</issue>
			<fpage>231</fpage>
			<lpage>244</lpage>
			<abstract>
				<p>In this paper two popular time series prediction methods – the Auto Regression Moving Average (ARMA) and the multilayer perceptron (MLP) – are compared while forecasting seven real world economical time series. It is shown that the prediction accuracy of both methods is poor in ill-structured problems. In the well-structured cases, when prediction accuracy is high, the MLP predicts better providing lower mean prediction error.</p>
			</abstract>
			<kwd-group>
				<label>Keywords</label>
				<kwd>ARMA model</kwd>
				<kwd>multilayer perceptron</kwd>
				<kwd>comparison</kwd>
				<kwd>prediction</kwd>
				<kwd>forecast</kwd>
				<kwd>economic time series</kwd>
			</kwd-group>
		</article-meta>
	</front>
</article>