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dc.contributor.authorTrappey, Charles V.en_US
dc.contributor.authorWu, Hsin-yingen_US
dc.date.accessioned2014-12-08T15:11:43Z-
dc.date.available2014-12-08T15:11:43Z-
dc.date.issued2007en_US
dc.identifier.isbn978-1-84628-975-0en_US
dc.identifier.urihttp://hdl.handle.net/11536/8979-
dc.identifier.urihttp://dx.doi.org/10.1007/978-1-84628-976-7_87en_US
dc.description.abstractMany successful technology forecasting models have been developed but little research has explored the relationship between sample set size and forecast prediction accuracy. This research studies the forecast accuracy of large and small data sets using the simple logisticl, Gompertz, and the extended logistic models. The performance of the models were evaluated using the mean absolute deviation and the root mean square error. A time series dataset of four electronic products and services were used to evaluate the model performance. The result shows that the extended logistic model fits large and small datasets better than the simple logistic and Gompertz models. The findings also show that that the extended logistic model is well suited to predict market growth with limited historical data as is typically the case for short lifecycle products and services.en_US
dc.language.isoen_USen_US
dc.subjectextended logistic modelen_US
dc.subjecttechnology forecastingen_US
dc.titleAn evaluation of the extended logistic, simple logistic, and gompertz models for forecasting short lifecycle products and servicesen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/978-1-84628-976-7_87en_US
dc.identifier.journalComplex Systems Concurrent Engineering: Collaboration, Technology Innovation and Sustainabilityen_US
dc.citation.spage793en_US
dc.citation.epage800en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000248899900087-
顯示於類別:會議論文


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