The corn output in a time series prediction model
This paper selected the raw data of the corn output of Dehui City, Jilin Province from 1990 to 2000, through data cleansing, data conversion and data integration technologies obtains time series data set, choosing the appropriate time series methods ARIMA(Autoregressive Integrated Moving Average) to...
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| creator | Guifen Chen Xingmei Xu Guowei Wang Hang Chen |
| description | This paper selected the raw data of the corn output of Dehui City, Jilin Province from 1990 to 2000, through data cleansing, data conversion and data integration technologies obtains time series data set, choosing the appropriate time series methods ARIMA(Autoregressive Integrated Moving Average) to confirm the corn output in a time series prediction model. The experimental results show that comparing the actual output and the prediction achieved by the model of corn output from 2001 to 2003, the error is very small; the relative error can be controlled within 5%. This proves that the ARIMA(2,2,1) model can fairly predict the developing trend of the corn output in this region, and the result of the prediction can provide very important theory evidence for the agricultural production management department to make decisions. |
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The experimental results show that comparing the actual output and the prediction achieved by the model of corn output from 2001 to 2003, the error is very small; the relative error can be controlled within 5%. This proves that the ARIMA(2,2,1) model can fairly predict the developing trend of the corn output in this region, and the result of the prediction can provide very important theory evidence for the agricultural production management department to make decisions.</description><identifier>ISSN: 2154-4824</identifier><identifier>ISBN: 9781424496730</identifier><identifier>ISBN: 142449673X</identifier><identifier>EISSN: 2154-4832</identifier><identifier>EISBN: 9781889335421</identifier><identifier>EISBN: 1889335428</identifier><language>eng</language><publisher>IEEE</publisher><subject>Adaptation model ; Analytical models ; ARIMA ; Correlation ; Data models ; non-parameter ; prediction model ; Predictive models ; Production ; statistics ; time series ; Time series analysis</subject><ispartof>2010 World Automation Congress, 2010, p.283-286</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5665492$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,54922</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5665492$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Guifen Chen</creatorcontrib><creatorcontrib>Xingmei Xu</creatorcontrib><creatorcontrib>Guowei Wang</creatorcontrib><creatorcontrib>Hang Chen</creatorcontrib><title>The corn output in a time series prediction model</title><title>2010 World Automation Congress</title><addtitle>WAC</addtitle><description>This paper selected the raw data of the corn output of Dehui City, Jilin Province from 1990 to 2000, through data cleansing, data conversion and data integration technologies obtains time series data set, choosing the appropriate time series methods ARIMA(Autoregressive Integrated Moving Average) to confirm the corn output in a time series prediction model. The experimental results show that comparing the actual output and the prediction achieved by the model of corn output from 2001 to 2003, the error is very small; the relative error can be controlled within 5%. This proves that the ARIMA(2,2,1) model can fairly predict the developing trend of the corn output in this region, and the result of the prediction can provide very important theory evidence for the agricultural production management department to make decisions.</description><subject>Adaptation model</subject><subject>Analytical models</subject><subject>ARIMA</subject><subject>Correlation</subject><subject>Data models</subject><subject>non-parameter</subject><subject>prediction model</subject><subject>Predictive models</subject><subject>Production</subject><subject>statistics</subject><subject>time series</subject><subject>Time series analysis</subject><issn>2154-4824</issn><issn>2154-4832</issn><isbn>9781424496730</isbn><isbn>142449673X</isbn><isbn>9781889335421</isbn><isbn>1889335428</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9jstqwzAURNUXNCT-gm70Awbp6kqWliX0BYFsvA-yfEUV4geysujf15DS2QzDgeHcsco1VlrrlNII8p5tQGqs0Sp4uDEERGcaJR7_GeAzq5blLNbodSqxYbL9Jh6mPPLpWuZr4Wnknpc0EF8oJ1r4nKlPoaRp5MPU02XHnqK_LFT99Za172_t_rM-HD--9q-HOjlRam8xmj6QjBIEqY6aoIPsvA_GoQkCVgcXLPUOgFYV28QopDFgtHREndqyl9ttIqLTnNPg889JG6PRgfoFdCVDZg</recordid><startdate>201009</startdate><enddate>201009</enddate><creator>Guifen Chen</creator><creator>Xingmei Xu</creator><creator>Guowei Wang</creator><creator>Hang Chen</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201009</creationdate><title>The corn output in a time series prediction model</title><author>Guifen Chen ; Xingmei Xu ; Guowei Wang ; Hang Chen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-a84f6dce1f120e3be7c5c1baac6946c020009c8ed922e82387ff016626519eeb3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Adaptation model</topic><topic>Analytical models</topic><topic>ARIMA</topic><topic>Correlation</topic><topic>Data models</topic><topic>non-parameter</topic><topic>prediction model</topic><topic>Predictive models</topic><topic>Production</topic><topic>statistics</topic><topic>time series</topic><topic>Time series analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Guifen Chen</creatorcontrib><creatorcontrib>Xingmei Xu</creatorcontrib><creatorcontrib>Guowei Wang</creatorcontrib><creatorcontrib>Hang Chen</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Guifen Chen</au><au>Xingmei Xu</au><au>Guowei Wang</au><au>Hang Chen</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The corn output in a time series prediction model</atitle><btitle>2010 World Automation Congress</btitle><stitle>WAC</stitle><date>2010-09</date><risdate>2010</risdate><spage>283</spage><epage>286</epage><pages>283-286</pages><issn>2154-4824</issn><eissn>2154-4832</eissn><isbn>9781424496730</isbn><isbn>142449673X</isbn><eisbn>9781889335421</eisbn><eisbn>1889335428</eisbn><abstract>This paper selected the raw data of the corn output of Dehui City, Jilin Province from 1990 to 2000, through data cleansing, data conversion and data integration technologies obtains time series data set, choosing the appropriate time series methods ARIMA(Autoregressive Integrated Moving Average) to confirm the corn output in a time series prediction model. The experimental results show that comparing the actual output and the prediction achieved by the model of corn output from 2001 to 2003, the error is very small; the relative error can be controlled within 5%. This proves that the ARIMA(2,2,1) model can fairly predict the developing trend of the corn output in this region, and the result of the prediction can provide very important theory evidence for the agricultural production management department to make decisions.</abstract><pub>IEEE</pub><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Adaptation model Analytical models ARIMA Correlation Data models non-parameter prediction model Predictive models Production statistics time series Time series analysis |
| title | The corn output in a time series prediction model |
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