21 Mar 2023
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Developing good research habits
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Monash
|
13 Mar 2023
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Feasts and fables: Time series analysis using R
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Reserve Bank of Australia
|
06 Dec 2022
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Exploratory time series analysis using R
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WOMBAT 2022 (online)
|
15 Nov 2022
|
Forecasting ensembles and combinations
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US CDC
|
09 Nov 2022
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Time series analysis & forecasting using R
|
ANU, Canberra
|
08 Nov 2022
|
Forecasting the future and the future of forecasting
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ANU, Canberra
|
23 Sep 2022
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Visualization of complex seasonal patterns in time series
|
University of Padua, Italy
|
31 Aug 2022
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Forecasting the old-age dependency ratio to determine a sustainable pension age
|
Misurina, Italy
|
23 Aug 2022
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Decomposing time series with complex seasonality
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COMPSTAT 2022, Bologna, Italy
|
10 Jul 2022
|
Creating social good for forecasters
|
Oxford, UK
|
02 Feb 2022
|
Feature-based time series analysis
|
Statistical Society of Canada
|
17 Jan 2022
|
Forecasting the future and the future of forecasting
|
Blakers Lecture, Canberra
|
17 Nov 2021
|
Feasts and fables: modern tools for time series analysis
|
Cornish Lecture, Adelaide
|
03 Nov 2021
|
Uncertain futures: AAS2021
|
Australian Academy of Science
|
28 Sep 2021
|
Feasts and fables: Time series analysis using R
|
Federal Reserve Bank, USA
|
16 Sep 2021
|
The geometry of forecast reconciliation
|
Macquarie University, Sydney
|
10 Sep 2021
|
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
|
Online
|
17 Aug 2021
|
Uncertain futures: what can we forecast and when should we give up?
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ACEMS
|
29 Jun 2021
|
Probabilistic ensemble forecasting of Australian COVID-19 cases
|
ISF 2021
|
06 May 2021
|
Forecasting elements that stand the test of time
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Online
|
12 Apr 2021
|
Seriously social podcast
|
Online
|
06 Feb 2021
|
Forecasting impact podcast
|
Online
|
25 Nov 2020
|
ASSA New Fellows Presentations
|
Academy of the Social Sciences in Australia
|
03 Nov 2020
|
Self-promotion for researchers
|
ACEMS
|
28 Oct 2020
|
COVID-19 impacts on our energy system
|
Energy Research Institutes Council, Melbourne
|
27 Oct 2020
|
Ten years of forecast reconciliation
|
ISF 2020
|
14 Aug 2020
|
Ensemble forecasts using fable
|
New York R conference, USA
|
19 Jul 2020
|
Podcast episode: the curious quant
|
Online
|
27 May 2020
|
Forecasting the Future & the Future of Forecasting
|
Online
|
30 Jan 2020
|
How Rmarkdown changed my life
|
San Francisco, USA
|
27 Jan 2020
|
Tidy time series & forecasting in R
|
San Francisco, USA
|
29 Oct 2019
|
The journal game
|
Monash
|
09 Oct 2019
|
Forecasts are always wrong (but we need them anyway)
|
Online
|
27 Sep 2019
|
Tidy time series analysis in R
|
Online
|
27 Sep 2019
|
Feature-based time series analysis
|
University of NSW
|
26 Sep 2019
|
Tidy time series analysis in R
|
University of Cardiff, Wales
|
21 Aug 2019
|
Forecasting is not prophecy: dealing with high-dimensional probabilistic forecasts in practice
|
ISI-WSC 2019, Kuala Lumpur, Malaysia
|
17 Aug 2019
|
High-dimensional time series analysis
|
Kuala Lumpur, Malaysia
|
19 Jun 2019
|
A feast of time series tools
|
ISF 2019, Thessaloniki, Greece
|
19 Jun 2019
|
Advancing forecasting research and practice
|
ISF 2019, Thessaloniki, Greece
|
25 Jan 2019
|
Feature-based forecasting algorithms for large collections of time series
|
ACEMS
|
11 Dec 2018
|
Data visualization for functional time series
|
Creswick, Australia
|
09 Dec 2018
|
Seasonal functional autoregressive models
|
University of Melbourne
|
05 Dec 2018
|
High-dimensional time series analysis
|
AAERS, Sydney
|
30 Nov 2018
|
Forecasting competitions
|
Monash
|
26 Nov 2018
|
Feature-based time series analysis
|
Monash
|
16 Oct 2018
|
Writing for Researchers
|
ACEMS
|
18 Sep 2018
|
Forecasting the future of the power industry: What can you learn from smart meter data?
|
Monash
|
13 Jul 2018
|
Tidy forecasting in R
|
useR 2018, Brisbane, Australia
|
21 Jun 2018
|
Feature-based time series analysis
|
New York R Meetup, USA
|
19 Jun 2018
|
Tidy forecasting in R
|
ISF 2018, Boulder, USA
|
09 Apr 2018
|
High dimensional time series analysis
|
ABS, Canberra
|
23 Mar 2018
|
Research++: what you should know about being a researcher but probably don’t
|
Monash
|
13 Dec 2017
|
Probabilistic outlier detection and visualization of smart metre data
|
Auckland, New Zealand
|
18 Nov 2017
|
2017 Beijing Workshop on Forecasting
|
Central University of Finance and Economics, Beijing, China
|
01 Nov 2017
|
High dimensional time series analysis
|
ACEMS
|
12 Oct 2017
|
Analysing sub-daily time series data
|
Melbourne R Users Group
|
21 Sep 2017
|
High-dimensional time series
|
Monash
|
25 Aug 2017
|
Biggish time series data
|
University of NSW, Sydney
|
25 Aug 2017
|
Optimal forecast reconcilation
|
University of NSW
|
11 Aug 2017
|
Visualizing and forecasting big time series data
|
ICML 2017, Sydney, Australia
|
14 Jul 2017
|
Using data to tackle poverty
|
Monash
|
22 Jun 2017
|
Probabilistic outlier detection and visualization of smart metre data
|
ISEA 2017, Cairns, Australia
|
18 Apr 2017
|
Follow-up Forecasting Forum
|
Eindhoven, Netherlands
|
04 Apr 2017
|
Software for honours students
|
Monash
|
21 Mar 2017
|
Probabilistic energy forecasting for smart grids and buildings
|
University of Melbourne
|
13 Oct 2016
|
Reconciling forecasts: the hts and thief packages
|
eRum2016, Poznań, Poland
|
15 Sep 2016
|
Forecasting large collections of related time series
|
German Statistical Week, Augsburg, Germany
|
20 Jun 2016
|
Exploring time series collections used for forecast evaluation
|
ISF 2016, Santander, Spain
|
06 May 2016
|
Automatic foRecasting using R
|
MeDaScIn 2016, Melbourne
|
18 Feb 2016
|
Making forecasting easier: forecast v7 for R
|
WOMBAT 2016
|
25 Oct 2015
|
Forecasting big time series data using R
|
Nanchang, China
|
06 Oct 2015
|
Optimal forecast reconciliation for big time series data
|
Stanford University, USA
|
05 Oct 2015
|
Google workshop: Forecasting and visualizing big time series data
|
Google, Mountain View, California, USA
|
17 Aug 2015
|
Machine learning bootcamp
|
Monash
|
30 Jun 2015
|
Exploring the feature space of large collections of time series
|
Banff, Canada
|
27 Jun 2015
|
Exploring the boundaries of predictability: what can we forecast, and when should we give up?
|
Yahoo Campus, Sunnyvale, California
|
25 Jun 2015
|
Automatic algorithms for time series forecasting
|
Google, Mountain View, California, USA
|
23 Jun 2015
|
MEFM: An R package for long-term probabilistic forecasting of electricity demand
|
ISF 2015, Riverside, USA
|
19 Jun 2015
|
Probabilistic forecasting of peak electricity demand
|
Southern California Edison, USA
|
26 May 2015
|
Visualization of big time series data
|
Statistical Society of Australia, Melbourne
|
22 May 2015
|
Probabilistic forecasting of long-term peak electricity demand
|
Monash
|
23 Feb 2015
|
Visualization and forecasting of big time series data
|
QUT, Brisbane
|
13 Jan 2015
|
Visualizing and forecasting big time series data
|
Academia Sinica, Taiwan
|
08 Dec 2014
|
Am I a data scientist?
|
Statistical Society of Australia, Melbourne
|
23 Sep 2014
|
Forecasting: principles and practice (UWA course)
|
University of Western Australia
|
01 Jul 2014
|
Fast computation of reconciled forecasts in hierarchical and grouped time series
|
ISF 2014, Rotterdam, Netherlands
|
24 Jun 2014
|
Functional time series with applications in demography
|
Humboldt University, Berlin, Germany
|
17 Jun 2014
|
Challenges in forecasting peak electricity demand
|
Valais, Switzerland
|
30 May 2014
|
State space models
|
ABS, Canberra
|
13 Feb 2014
|
Automatic time series forecasting
|
Granada, Spain
|
11 Oct 2013
|
Coherent mortality forecasting using functional time series
|
Macquarie University, Sydney
|
10 Oct 2013
|
Forecasting hierarchical time series
|
University of Sydney
|
04 Jul 2013
|
R tools for hierarchical time series
|
UseR! 2013, Albacete, Spain
|
25 Jun 2013
|
Forecasting without forecasters
|
ISF 2013, Seoul, South Korea
|
07 Feb 2013
|
Man vs wild data
|
University of Melbourne
|
20 Nov 2012
|
SimpleR: tips, tricks and tools
|
Melbourne R Users Group
|
19 Jun 2012
|
Advances in automatic time series forecasting
|
COMPSTAT 2012, Cyprus
|
27 Oct 2011
|
Forecasting time series using R
|
Melbourne R Users Group
|
03 Oct 2011
|
Forecasting electricity demand distributions using a semiparametric additive model
|
EDF, Paris, France
|
15 Jun 2011
|
Evaluating extreme quantile forecasts
|
ISF 2011, Prague, Czech Republic
|
07 Sep 2010
|
Demographic forecasting using functional data analysis
|
Wollongong, Australia
|
09 Jun 2010
|
Coherent functional forecasts of mortality rates and life expectancy
|
ISF 2010, San Diego, USA
|
25 Jun 2009
|
English academic writing
|
University of Fuzhou, China
|
23 Jun 2009
|
Extreme forecasting
|
ISF 2009, Hong Kong
|
01 May 2009
|
Statistical support for HDR students
|
University of Melbourne
|
18 Jul 2008
|
Forecasting and the importance of being uncertain
|
Monash
|
29 Jun 2008
|
Building R packages for Windows
|
R workshop, Melbourne
|
29 Jun 2008
|
Time series and forecasting in R
|
R workshop, Melbourne
|
19 Jun 2008
|
Bagplots, boxplots and outlier detection for functional data
|
ASC 2008, Melbourne
|
15 Jun 2008
|
Exponential smoothing and non-negative data
|
ISF 2008, Nice, France
|
22 Feb 2008
|
Forecasting functional time series
|
Australian Academy of Science, Canberra
|
27 Nov 2007
|
Population forecasting and the importance of being uncertain
|
Knibbs Lecture, Canberra
|
25 Oct 2007
|
Graduation address
|
Monash
|
25 Jun 2007
|
Forecasting medium- and long-term peak electricity demand
|
ISF 2007, New York, USA
|
22 Feb 2007
|
Stochastic population forecasts using functional data models
|
University of Melbourne
|
26 Oct 2006
|
Forecasting and the importance of being uncertain
|
Belz Lecture, Melbourne
|
26 Jun 2006
|
Automatic time series forecasting
|
UseR! 2006, Vienna, Austria
|
20 Jun 2006
|
Optimal combination forecasts for hierarchical time series
|
ISF 2006, Santander, Spain
|