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【学术通知】澳大利亚新英格兰大学教授Raymond Chiong:AI for Commodity Price Forecasting and Market Anomaly Detection

  • 发布日期:2025-07-03
  • 点击数:

  

2025年第49期(总第1090期)

演讲主题:AI for Commodity Price Forecasting and Market Anomaly Detection

主讲人:Raymond Chiong 澳大利亚新英格兰大学教授

主持人:鲍玉昆 信息管理与数据科学系教授

活动时间:2025年7月4日(周五)10:00-12:00

活动地点:管院大楼121室

主讲人简介:

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.

活动简介:

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.

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