Summary of Quantitative Technology Forecasting: a Review Of Trend Extrapolation Methods, by Peng-hung Tsai et al.
Quantitative Technology Forecasting: a Review of Trend Extrapolation Methods
by Peng-Hung Tsai, Daniel Berleant, Richard S. Segall, Hyacinthe Aboudja, Venkata Jaipal R. Batthula, Sheela Duggirala, Michael Howell
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper undertakes a systematic review of quantitative trend extrapolation techniques used in technology forecasting. By analyzing 25 relevant studies, it categorizes the employed methods into different groups, showing that growth curves and time series approaches remain popular over the past decade. Newer machine learning-based hybrid models have emerged recently, but their superiority to traditional methods remains uncertain, with more research needed to determine their effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can predict future technological advancements by reviewing many studies on a specific way of doing this called quantitative trend extrapolation. It found that some old ways of doing this, like looking at patterns in data, are still being used. But it also found that new approaches, like using machine learning to combine different methods, are becoming more popular. The study wants us to know that we need more research to see if these new methods are really better than the old ones. |
Keywords
» Artificial intelligence » Machine learning » Time series
