# Quantitative Technology Forecasting: a Review of Trend Extrapolation Methods

arXiv: https://arxiv.org/abs/2401.02549 · HTML: https://mlsanity.com/paper/2401.02549/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 4 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Deep Learning in Robotics: A Review of Recent Research](https://mlsanity.com/paper/1707.07217/) (2017)
- [Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories](https://mlsanity.com/paper/1808.05053/) (2018)
- [A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases](https://mlsanity.com/paper/1305.0377/) (2013)

## Built on by (most cited first, 1 of 1)

- [Trend Extrapolation for Technology Forecasting: Leveraging LSTM Neural Networks for Trend Analysis of Space Exploration Vessels](https://mlsanity.com/paper/2512.19727/) (2025)

## Abstract (arXiv)

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.