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Abstract : Technology evolution forecasting based on historical data processing is a useful tool for quantitative analysis in technology planning and roadmapping. While previous efforts focused mainly on one-dimensional forecasting, real technical systems require the evaluation of multiple and conflicting figures of merit at the same time, such as cost and performance. This paper presents a methodology for technology forecasting based on Pareto (efficient) frontier estimation algorithms and multiple regressions in presence of at least two conflicting figures of
merits. A tool was developed on the basis of the approach presented in this paper. The methodology is illustrated with a case study from the automotive industry. The paper also shows the validation of the methodology and the estimation of the forecast accuracy adopting a backward testing procedure.
Ilya yuskevich, Rob A. Vingerhoeds, Alessandro Golkar. Two-dimensional Pareto frontier forecasting for technology planning and roadmapping. 2018 Systems Conferences (Syscon), Apr 2018, Vancouvre, Canada. pp. 560-566. ⟨hal-01804001⟩