paper-with-me

Papers

Quantitative Technology Forecasting: a Review of Trend Extrapolation Methods

2024-01-04 · Peng-Hung Tsai, Daniel Berleant, Richard S. Segall, Hyacinthe Aboudja, Venkata Jaipal R. Batthula, Sheela Duggirala, Michael Howell

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.

📄 PDF Abstract BibTeX arXiv:2401.02549

Code (0)

등록된 구현이 없습니다.

Tasks

Time Series

Similar Papers 제목 키워드 기반

Trend Extrapolation for Technology Forecasting: Leveraging LSTM Neural Networks for Trend Analysis of Space Exploration Vessels

2025-12-17 · Peng-Hung Tsai, Daniel Berleant arxiv

Forecasting technological advancement in complex domains such as space exploration presents significant challenges due to the intricate interaction of technical, economic, and policy-related factors. The field of technol…

Trend-Adjusted Time Series Models with an Application to Gold Price Forecasting

2026-01-19 · Sina Kazemdehbashi arxiv

Time series data play a critical role in various fields, including finance, healthcare, marketing, and engineering. A wide range of techniques (from classical statistical models to neural network-based approaches such as…

Time Series Forecasting

HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information

2021-10-26 · Wentao Xu, Weiqing Liu, Lewen Wang, Yingce Xia 외

Stock trend forecasting, which forecasts stock prices' future trends, plays an essential role in investment. The stocks in a market can share information so that their stock prices are highly correlated. Several methods …

Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs

2021-06-24 · Angelo Salatino, Andrea Mannocci, Francesco Osborne

Analysing research trends and predicting their impact on academia and industry is crucial to gain a deeper understanding of the advances in a research field and to inform critical decisions about research funding and tec…

ArticlesKnowledge Graphs

A Stochastic Time Series Model for Predicting Financial Trends using NLP

2021-02-02 · Pratyush Muthukumar, Jie Zhong

Stock price forecasting is a highly complex and vitally important field of research. Recent advancements in deep neural network technology allow researchers to develop highly accurate models to predict financial trends. …

Generative Adversarial NetworkSentiment AnalysisTime SeriesTime Series Analysis