{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:49:13Z","timestamp":1777704553055,"version":"3.51.4"},"reference-count":12,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T00:00:00Z","timestamp":1586908800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,6,25]]},"abstract":"<jats:p>\u00a0Gray fuzzy prediction model is suitable for small-sample-size prediction. The real per capita disposable income of urban residents in Hebei Province used as an example, and samples 3\u201335 in length selected, the influence of sample length on prediction performance of the GM (1,1) model were investigated. Sample length presents a nonlinear relationship with the predicted relative error of the model. Compared with large samples with lengths more than 15, small samples with lengths below 15 are suitable to establish the gray fuzzy prediction model. Small samples with length of 8\u201313 are applicable to three-step prediction. Sample lengths suitable for modeling were proposed, and the above conclusions provide a certain theoretical foundation and guidance for the research and application of gray fuzzy prediction in the future.<\/jats:p>","DOI":"10.3233\/jifs-179752","type":"journal-article","created":{"date-parts":[[2020,4,17]],"date-time":"2020-04-17T12:20:56Z","timestamp":1587126056000},"page":"6745-6754","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Influence of sample length on gray fuzzy prediction performance"],"prefix":"10.1177","volume":"38","author":[{"given":"Jianzhong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Business, Hebei Agriculture University, P.R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yashuo","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Economics, Hebei University, P.R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Economics, Hebei University, P.R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,4,15]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-6911(82)80025-X"},{"issue":"4","key":"e_1_3_1_3_2","first-page":"122","article-title":"Grey GM(1,1) power pharmacokinetics model coupling self-memory principle of dynamic system","volume":"26","author":"Guo X.","year":"2014","unstructured":"GuoX., LiuS., FangZ., Grey GM(1,1) power pharmacokinetics model coupling self-memory principle of dynamic system, Journal of Grey System26(4) (2014), 122\u2013138.","journal-title":"Journal of Grey System"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph14030262"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1057\/s41274-016-0150-y"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcm.2012.06.013"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2014.08.006"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2015.2426133"},{"issue":"19","key":"e_1_3_1_9_2","first-page":"14","article-title":"A new method for constructing background value of grey prediction model","author":"Yang X.L.","year":"2018","unstructured":"YangX.L., ZhouM., ZengB., A new method for constructing background value of grey prediction model, Statistics & Decision (19) (2018), 14\u201318.","journal-title":"Statistics & Decision"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2017.07.003"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2013.01.018"},{"issue":"08","key":"e_1_3_1_12_2","first-page":"60","article-title":"Forecasting urban population density using a multi-variable grey system model","volume":"33","author":"Wu H.A.","year":"2018","unstructured":"WuH.A., ZengB., PengY., ZhouM., Forecasting urban population density using a multi-variable grey system model, Statistics & Information Forum33(08) (2018), 60\u201367.","journal-title":"Statistics & Information Forum"},{"issue":"07","key":"e_1_3_1_13_2","first-page":"41","article-title":"Research on industrial chain synergy degree in express and e-commerce industry in big data era","volume":"35","author":"Shen S.D.","year":"2018","unstructured":"ShenS.D., KangX.Q., Research on industrial chain synergy degree in express and e-commerce industry in big data era, The Journal of Quantitative & Technical Economics35(07) (2018), 41\u201358.","journal-title":"The Journal of Quantitative & Technical Economics"}],"container-title":["Journal of Intelligent &amp; 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