  Quantitative technology forecasting uses quantitative methods to understand
and project technological changes. It is a broad field encompassing many
different techniques and has been applied to a vast range of technologies. A
widely used approach in this field is trend extrapolation. Based on the
publications available to us, there has been little or no attempt made to
systematically review the empirical evidence on quantitative trend
extrapolation techniques. This study attempts to close this gap by conducting a
systematic review of technology forecasting literature addressing the
application of quantitative trend extrapolation techniques. We identified 25
studies relevant to the objective of this research and classified the
techniques used in the studies into different categories, among which growth
curves and time series methods were shown to remain popular over the past
decade, while newer methods, such as machine learning-based hybrid models, have
emerged in recent years. As more effort and evidence are needed to determine if
hybrid models are superior to traditional methods, we expect to see a growing
trend in the development and application of hybrid models to technology
forecasting.
