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Grant Award View - GA281515
Feature Learning for High-dimensional Functional Time Series
GA ID:
GA281515
Agency:
Australian Research Council
Approval Date:
19-Jan-2023
Publish Date:
1-Feb-2023
Category:
Humanities, Arts and Social Sciences (HASS) Research
Grant Term:
19-Jan-2023 to 31-Dec-2025
Value (AUD):
$353,000.00
(GST inclusive where applicable)
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 22/23 Discovery
Grant Program:
Discovery Projects
Grant Activity:
Feature Learning for High-dimensional Functional Time Series
Purpose:
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.
GO ID:
GO Title:
Discovery Projects for funding commencing in 2023
Internal Reference ID:
DP23 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
The Australian National University
Recipient ABN:
52 234 063 906
Grant Recipient Location
Suburb:
ACTON
Town/City:
ACTON
Postcode:
2601
State/Territory:
ACT
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
ACT
Postcode:
2601
Country:
AUSTRALIA