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Generating Technology Evolution Prediction Intervals With Bootstrap Method

[+] Author Affiliations
Guanglu Zhang, Douglas Allaire, Daniel A. McAdams, Venkatesh Shankar

Texas A&M University, College Station, TX

Paper No. DETC2018-85259, pp. V007T06A057; 8 pages
doi:10.1115/DETC2018-85259
From:
  • ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 7: 30th International Conference on Design Theory and Methodology
  • Quebec City, Quebec, Canada, August 26–29, 2018
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5184-5
  • Copyright © 2018 by ASME

abstract

Technology evolution prediction, or technological forecasting, is critical for designers to make important decisions during product development planning such as R&D investment and outsourcing. In practice, designers want to supplement point forecast by prediction intervals to assess future uncertainty and make contingency plans. Available technology evolution data is a time series but is generally with non-uniform spacing. Existing methods associated with typical time series models assume uniformly spaced data, so these methods cannot be used to construct prediction intervals for technology evolution prediction. In this paper, we develop a generic method that use bootstrapping to generate prediction intervals for technology evolution. The method we develop can be applied to any technology evolution prediction model. We consider parameter uncertainty and data uncertainty and establish their empirical probability distributions. We determine an appropriate confidence level α to generate prediction intervals through a holdout sample analysis rather than set α = 0.05 as is typically done in the literature. We validate our method to generate the prediction intervals through a case study of central processing unit transistor count evolution. The case study shows that the prediction intervals generated by our method cover every actual data point in a holdout sample test. To apply our method in practice, we outline four steps for designers to generate prediction intervals for technology evolution prediction.

Copyright © 2018 by ASME

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