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Grant Award View - GA28911

Efficient and effective analytics for real-world time series forecasting

Contact Details

ARC NCGP General Enquiries

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02 6287 6600

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GA ID:
GA28911
Agency:
Australian Research Council
Approval Date:
27-Nov-2018
Publish Date:
18-Dec-2018
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
18-Feb-2019 to 31-Dec-2022
Original: 1-Jan-2019 to 31-Dec-2021
Value (AUD):
$377,829.00 (GST inclusive where applicable)
Variations:

One-off/Ad hoc:
No
Aggregate Grant Award:
No

PBS Program Name:
ARC 18/19 Discovery
Grant Program:
Discovery Early Career Researcher Award
Grant Activity:
Efficient and effective analytics for real-world time series forecasting
Purpose:
This project aims to create efficient, effective techniques that provide accurate forecasts for heterogeneous sets of time series of varying sizes. Exploiting similarities between time series means using many related series, not larger series when building forecasts. The expected outcomes should be innovative methods that improve accuracy and allow forecasting with shorter time series. The project addresses the need to exploit properties of big data accurately in a short time frame, which is transforming many industries. This should enable more accurate and reliable forecasts across industries as diverse as retail, food manufacturing, transport, mining, tourism, energy, and technology.

GO ID:
GO Title:
Discovery Early Career Researcher Award commencing in 2019
Internal Reference ID:
DE19 Round 1
Selection Process:
Targeted or Restricted Competitive

Confidentiality - Contract:
No
Confidentiality - Outputs:
No

Grant Recipient Details

Recipient Name:
Monash University
Recipient ABN:
12 377 614 012

Grant Recipient Location

Suburb:
MULGRAVE
Town/City:
MULGRAVE
Postcode:
3170
State/Territory:
VIC
Country:
AUSTRALIA

Grant Delivery Location

State/Territory:
VIC
Postcode:
3170
Country:
AUSTRALIA

Contact Details

ARC NCGP General Enquiries

:
02 6287 6600

: