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An operations research software <jats:italic>LINGO<\/jats:italic> is employed to solve the non\u2010linear optimization model.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The results show that the optimized GPM(1,1) model can flexibly adjust the parameters to make the forecasting results more in line with the actual data; therefore, for a given small sample oscillating series, if an appropriate way to find the optimal parameters is taken, accurate predictions should be obtained.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>The modeling approach proposed in the paper can be used to forecast new product sales, new industry development trend, equipment remaining life, disaster emergency material demand, etc.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The paper extends the application range of the grey model for forecasting small sample oscillating series by using grey power model GPM(1,1).<\/jats:p><\/jats:sec>","DOI":"10.1108\/20439371211260162","type":"journal-article","created":{"date-parts":[[2014,11,13]],"date-time":"2014-11-13T12:10:36Z","timestamp":1415880636000},"page":"197-206","source":"Crossref","is-referenced-by-count":5,"title":["The optimized GPM(1,1) for forecasting small sample oscillating series"],"prefix":"10.1108","volume":"2","author":[{"given":"Zheng\u2010Xin","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yao\u2010Guo","family":"Dang","sequence":"additional","affiliation":[]},{"given":"Shawei","family":"He","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2021010620405410400_b6","doi-asserted-by":"crossref","unstructured":"Bo, L. and Wei, Y. 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