Using ARIMA Models to Predict Prison Populations
In this study a time-series model for predicting Louisiana's prison population was developed using the iterative Box—Jenkins modeling methodology—identification, estimation, and diagnostic checking. The time-series forecasts were contrasted with results of regression models and an exponential s...
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| Veröffentlicht in: | Journal of quantitative criminology 1986-09, Vol.2 (3), p.251-264 |
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| container_end_page | 264 |
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| container_issue | 3 |
| container_start_page | 251 |
| container_title | Journal of quantitative criminology |
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| creator | Lin, Bin-Shan MacKenzie, Doris Layton Gulledge, Thomas R. |
| description | In this study a time-series model for predicting Louisiana's prison population was developed using the iterative Box—Jenkins modeling methodology—identification, estimation, and diagnostic checking. The time-series forecasts were contrasted with results of regression models and an exponential smoothing model. The results indicate that the time-series model is the superior model as indicated by the usual measures of predictive accuracy. When compared with actual data the predictions appeared sufficiently adequate to meet the needs of the correctional system for short-term planning. |
| doi_str_mv | 10.1007/BF01066529 |
| format | Article |
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The time-series forecasts were contrasted with results of regression models and an exponential smoothing model. The results indicate that the time-series model is the superior model as indicated by the usual measures of predictive accuracy. When compared with actual data the predictions appeared sufficiently adequate to meet the needs of the correctional system for short-term planning.</description><identifier>ISSN: 0748-4518</identifier><identifier>EISSN: 1573-7799</identifier><identifier>DOI: 10.1007/BF01066529</identifier><identifier>CODEN: JQCRE6</identifier><language>eng</language><publisher>New York: Plenum Press</publisher><subject>Analytical forecasting ; Economic Planning ; Forecasting models ; Louisiana ; Modeling ; Outliers ; Prediction Models ; Prisoners ; Prisons ; Regression analysis ; Social research ; Statistical forecasts ; Time series forecasting ; Time series models ; Weather forecasting</subject><ispartof>Journal of quantitative criminology, 1986-09, Vol.2 (3), p.251-264</ispartof><rights>1986 Plenum Publishing Corporation</rights><rights>Copyright Plenum Publishing Corporation Sep 1986</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c257t-11d516bd4f23824ddfb750d2fb21534601134cb4e3bf19568714e104cc57d3a43</citedby><cites>FETCH-LOGICAL-c257t-11d516bd4f23824ddfb750d2fb21534601134cb4e3bf19568714e104cc57d3a43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/23365635$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/23365635$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,27903,27904,30978,33753,33754,57996,58229</link.rule.ids></links><search><creatorcontrib>Lin, Bin-Shan</creatorcontrib><creatorcontrib>MacKenzie, Doris Layton</creatorcontrib><creatorcontrib>Gulledge, Thomas R.</creatorcontrib><title>Using ARIMA Models to Predict Prison Populations</title><title>Journal of quantitative criminology</title><description>In this study a time-series model for predicting Louisiana's prison population was developed using the iterative Box—Jenkins modeling methodology—identification, estimation, and diagnostic checking. The time-series forecasts were contrasted with results of regression models and an exponential smoothing model. The results indicate that the time-series model is the superior model as indicated by the usual measures of predictive accuracy. When compared with actual data the predictions appeared sufficiently adequate to meet the needs of the correctional system for short-term planning.</description><subject>Analytical forecasting</subject><subject>Economic Planning</subject><subject>Forecasting models</subject><subject>Louisiana</subject><subject>Modeling</subject><subject>Outliers</subject><subject>Prediction Models</subject><subject>Prisoners</subject><subject>Prisons</subject><subject>Regression analysis</subject><subject>Social research</subject><subject>Statistical forecasts</subject><subject>Time series forecasting</subject><subject>Time series models</subject><subject>Weather forecasting</subject><issn>0748-4518</issn><issn>1573-7799</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1986</creationdate><recordtype>article</recordtype><sourceid>7QJ</sourceid><sourceid>BHHNA</sourceid><recordid>eNp90M9LwzAcBfAgCs7pxbtQPIgI1e83yTdpj3P4Y7DhEHcubZNKR9fMpD3439sxUfDg6V0-PHiPsXOEWwTQd_ePgKAU8fSAjZC0iLVO00M2Ai2TWBImx-wkhDUApEnCRwxWoW7fo8nrbDGJFs7YJkSdi5bemrrshqyDa6Ol2_ZN3tWuDafsqMqbYM--c8xWjw9v0-d4_vI0m07mcclJdzGiIVSFkRUXCZfGVIUmMLwqOJKQChCFLAtpRVFhSirRKC2CLEvSRuRSjNnVvnfr3UdvQ5dt6lDapslb6_qQKSQSSUoDvP4XolaouSbOB3r5h65d79thRsZhOI5Lteu72aPSuxC8rbKtrze5_8wQst3J2e_JA77Y43XonP-RXAhFSpD4AngcdCM</recordid><startdate>19860901</startdate><enddate>19860901</enddate><creator>Lin, Bin-Shan</creator><creator>MacKenzie, Doris Layton</creator><creator>Gulledge, Thomas R.</creator><general>Plenum Press</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QJ</scope><scope>7U4</scope><scope>BHHNA</scope><scope>DWI</scope><scope>K7.</scope><scope>WZK</scope><scope>7U3</scope></search><sort><creationdate>19860901</creationdate><title>Using ARIMA Models to Predict Prison Populations</title><author>Lin, Bin-Shan ; MacKenzie, Doris Layton ; Gulledge, Thomas R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c257t-11d516bd4f23824ddfb750d2fb21534601134cb4e3bf19568714e104cc57d3a43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1986</creationdate><topic>Analytical forecasting</topic><topic>Economic Planning</topic><topic>Forecasting models</topic><topic>Louisiana</topic><topic>Modeling</topic><topic>Outliers</topic><topic>Prediction Models</topic><topic>Prisoners</topic><topic>Prisons</topic><topic>Regression analysis</topic><topic>Social research</topic><topic>Statistical forecasts</topic><topic>Time series forecasting</topic><topic>Time series models</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Bin-Shan</creatorcontrib><creatorcontrib>MacKenzie, Doris Layton</creatorcontrib><creatorcontrib>Gulledge, Thomas R.</creatorcontrib><collection>CrossRef</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>Sociological Abstracts (pre-2017)</collection><collection>Sociological Abstracts</collection><collection>Sociological Abstracts</collection><collection>ProQuest Criminal Justice (Alumni)</collection><collection>Sociological Abstracts (Ovid)</collection><collection>Social Services Abstracts</collection><jtitle>Journal of quantitative criminology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Bin-Shan</au><au>MacKenzie, Doris Layton</au><au>Gulledge, Thomas R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using ARIMA Models to Predict Prison Populations</atitle><jtitle>Journal of quantitative criminology</jtitle><date>1986-09-01</date><risdate>1986</risdate><volume>2</volume><issue>3</issue><spage>251</spage><epage>264</epage><pages>251-264</pages><issn>0748-4518</issn><eissn>1573-7799</eissn><coden>JQCRE6</coden><abstract>In this study a time-series model for predicting Louisiana's prison population was developed using the iterative Box—Jenkins modeling methodology—identification, estimation, and diagnostic checking. The time-series forecasts were contrasted with results of regression models and an exponential smoothing model. The results indicate that the time-series model is the superior model as indicated by the usual measures of predictive accuracy. When compared with actual data the predictions appeared sufficiently adequate to meet the needs of the correctional system for short-term planning.</abstract><cop>New York</cop><pub>Plenum Press</pub><doi>10.1007/BF01066529</doi><tpages>14</tpages></addata></record> |
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| ispartof | Journal of quantitative criminology, 1986-09, Vol.2 (3), p.251-264 |
| issn | 0748-4518 1573-7799 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_61553895 |
| source | HeinOnline Law Journal Library; Sociological Abstracts; Applied Social Sciences Index & Abstracts (ASSIA); Jstor Complete Legacy; SpringerLink Journals - AutoHoldings |
| subjects | Analytical forecasting Economic Planning Forecasting models Louisiana Modeling Outliers Prediction Models Prisoners Prisons Regression analysis Social research Statistical forecasts Time series forecasting Time series models Weather forecasting |
| title | Using ARIMA Models to Predict Prison Populations |
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