报告题目:AI for Commodity Price Forecasting and Market Anomaly Detection
报告人:Raymond Chiong 教授
邀请人:徐照光 副教授
时间:2025年06月20日,上午10:00
地点:经济管理学院 B305
报告人简介:
Raymond Chiong is a professor in artificial intelligence (AI) from Australia, affiliated with both the University of New England and University of Newcastle. He is known internationally for his work on the use of AI methods for computational modelling as well as addressing prediction and optimisation problems. He has produced more than 260 publications to date. His publications have been cited over 9,500 times according to Google Scholar, with an h-index of 50. He has also attracted over $5million in research and industry funding. He is ranked among the top 2% of most influential scientists in the world by the Stanford University/Mendeley List (since 2022) and among the top Computer Science researchers in Australia (https://research.com/scientists-rankings/computer-science/au). He is the Editor-in-Chief of Elsevier’s Computers in Industry journal. He also serves as an Editor for Elsevier’s Engineering Applications of Artificial Intelligence and an Associate Editor for the IEEE Transactions on Evolutionary Computation.
报告摘要:
This talk will explore the use of AI, specifically deep learning models, for multi-commodity price forecasting and market anomaly detection. The agricultural market will be presented as a case study by focusing on essential food commodities such as red chili, shallots, and rice. Deep learning models are applied to daily price data of these food commodities that often show unpredictable swings. A transformer model is used to predict the price trends with improved accuracy, while an attention-based autoencoder helps identify sudden and irregular changes in prices. Together, these AI methods provide early insights that can help small businesses and farmers make smarter decisions, avoid losses, and respond more effectively to market uncertainties.
上一条:Geopolitics and supply chain disruptions – Financial consequences of the 2018 ban on ZTE Corp.
下一条:数智时代的虚拟企业研究:历史、现状与展望
【关闭】