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Stochastic Modelling of Economic Time-Series Through Graphic Models and Machine Learning (ML) techniques to Identify Extreme Scenarios in Decision Making Project

Project Member(s): Su, S.

Funding or Partner Organisation: Commonwealth Scientific and Industrial Research Organisation (Data61) (Data61 CSIRO)
Commonwealth Scientific and Industrial Research Organisation (Data61) (Data61 CSIRO)

Start year: 2022

Summary: Long-term decision making under uncertainty is a critical and complex research topic impacting the current debates from climate risk, energy transition to superannuation strategies. The critical component of reaching optimal decision is on identifying and projecting future extreme scenarios by analysing patterns in historical data. The second critical component is on creating signal indices to indicate which extreme scenarios will likely happen. The current debate on possible energy transition strategies can directly benefit from the outcome of this research. Another direct application of this research will be the decumulation strategies that the baby-boomer retirees rely on to maximize retirement wellbeing and navigate the longevity risk. For this research work, we hope to develop new methods/models emerging in the fields of forecast via graphic modelling and Machine Learning (ML) techniques. We also hope that the outcome of this research can make major contributions from both theoretical and practical perspectives.

FOR Codes: Artificial intelligence not elsewhere classified, Expanding knowledge in engineering