汪思韦,湖南大学金融与统计学院副教授。2022年从北京大学获得应用经济学博士学位,现主持国家自然科学基金青年科学基金项目。主要研究领域为非参数计量建模、高维数据分析等,研究成果发表在Journal of Econometrics, Oxford Bulletin of Economics and Statistics,Economics Letters以及《系统工程理论与实践》上。
Pre-trained foundation models are making time-series forecasting more accessible and available, unlocking its benefits for smaller organizations with limited resources. Over the last year, we’ve seen ...
In the wake of the disruptive debut of DeepSeek-R1, reasoning models have been all the rage so far in 2025. IBM is now joining the party, with the debut today of its Granite 3.2 large language model ...
Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...
Ant International currently deploys the Falcon TST AI Model to forecast cashflow and FX exposure with more than 90% accuracy Ant International, a leading global digital payment, digitisation, and ...
What if you could predict the future—not just in abstract terms, but with actionable precision? From forecasting energy demand to anticipating retail trends, the ability to make accurate predictions ...