Dr Chen ZhiYuan and her research team (Dr Teo Wing Leong, Prof Sue Walker, Dr Mehdi Maqbool, Prof Ettikan Kandasamy Karuppiah, Ee Na Teoh and Oh Mei Shin) have successfully secured the Pump-priming grant for Novel Machine Learning Strategies for GST Impact on Agricultural Commodity Prices by Monitoring the Short-Term Fluctuations in Malaysia project.  The intention of this research is to study and design novel machine learning strategies to empirically and quantitatively identify GST impact on agricultural commodity prices by monitoring the short term fluctuations in Malaysia. The increasing availability of large amounts of agricultural commodity prices historical data and the need of performing accurate forecasting of short term fluctuations demands the definition of robust and efficient techniques able to infer from current observations. With the emergence of machine learning techniques, the solution for time series forecasting has largely shifted from statistical methods to machine learning area. However the formalization of forecasting on agricultural commodity prices problems, the role of the forecasting strategy for short term fluctuations have not been directly studied. Meanwhile, when implementing machine learning techniques, finding optimal parameters of learning algorithm, nonlinearity and avoiding curse of dimensionality are still biggest challenges. This proposal presents novel and efficient machine learning strategies to solve these problems by focusing 5 categories, 59 commodities.

Two short-term research assistant positions are available for this project; please contact Dr Chen ZhiYuan (zhiyuan.chen@nottingham.edu.my) for details.