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【明理讲堂2025年第21期】6-25 澳大利亚新英格兰大学(University of New England)及纽卡斯尔大学(University of Newcastle) Raymond Chiong 教授: AI for Commodity Price Forecasting and Market Anomaly Detection

时间:June 25, 2025, 10:00-11:30

地点:中关村主楼216

报告人:Raymond Chiong

报告人国籍:马来西亚

报告人职务:副院长

报告人职称:教授

报告人工作单位:澳大利亚新英格兰大学(University of New England)、纽卡斯尔大学

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.

报告内容简介:

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.

(承办:管理工程系、科研与学术交流中心)

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