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.

翻译：定量技术预测利用定量方法理解和预测技术变革。这是一个涵盖多种技术的广阔领域，已被广泛应用于各类技术的预测。趋势外推是该领域中广泛使用的方法。基于现有文献，目前几乎没有或完全没有系统地综述定量趋势外推技术实证证据的尝试。本研究旨在通过系统综述技术预测文献中定量趋势外推技术的应用，来填补这一空白。我们识别出25项与本研究目标相关的研究，并将其使用的技术分为不同类别。其中，增长曲线和时间序列方法在过去十年中仍保持流行，而基于机器学习的混合模型等新方法近年来逐渐兴起。由于需要更多努力和证据来确定混合模型是否优于传统方法，我们预计混合模型在技术预测中的开发和应用将呈现增长趋势。