Discovery Projects - Grant ID: DP230102250

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DP230102250

Funding Status
Active

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Funded Activity Summary

Feature Learning for High-dimensional Functional Time Series. This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algorithms for applied researchers. This project benefits not only advanced manufacturing by finding optimal stopping time for wood panel compression, but also superior forecasting for mortality in demography, climate data in environmental science, asset returns in finance, and electricity consumption in economics.

Funded Activity Details

Start Date: 01-01-2023

End Date: 31-12-2025

Funding Scheme: Discovery Projects

Funding Amount: $353,000.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Large and complex data theory | Applied statistics | Statistics | Statistical theory |

ANZSRC Socio-Economic Objective (SEO)

Other Keywords

Applied statistics | Climate Variability (Excl. Social Impacts) | Large and complex data theory | Statistical theory | Statistics | Superannuation and Insurance Services | Wood Products

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