An evaluation of the extended logistic, simple logistic, and gompertz models for forecasting short lifecycle products and services

Charles V. Trappey, Hsin Ying Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Many 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.

Original languageEnglish
Title of host publicationComplex Systems Concurrent Engineering
Subtitle of host publicationCollaboration, Technology Innovation and Sustainability
Pages793-800
Number of pages8
DOIs
StatePublished - 1 Dec 2007
Event14th ISPE International Conference on Concurrent Engineering, CE 2007 - Sao Jose dos Campos, SP, Brazil
Duration: 16 Jul 200720 Jul 2007

Publication series

NameComplex Systems Concurrent Engineering: Collaboration, Technology Innovation and Sustainability

Conference

Conference14th ISPE International Conference on Concurrent Engineering, CE 2007
CountryBrazil
CitySao Jose dos Campos, SP
Period16/07/0720/07/07

Keywords

  • Extended logistic model
  • Technology forecasting

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