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Timur Behlul, Anastasios Panagiotelis, George Athanasopoulos, Rob J Hyndman, Farshid Vahid

Abstract
A website that encourages and facilities the use of quantitative, publicly available Australian macroeconomic data is introduced. The Australian Macro Database hosted at ausmacrodata.org provides a user friendly front end for searching among over 40000 economic variables, sourced from the Australian Bureau of Statistics and the Reserve Bank of Australia. The search box, tags and categories used to facilitate data retrieval, are described in detail. Known issues with the website and future plans are discussed in the conclusion.

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  Tag: data science

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February 14th, 2017

The Australian Macro Database: An online resource for macroeconomic research in Australia

Timur Behlul, Anastasios Panagiotelis, George Athanasopoulos, Rob J Hyndman, Farshid Vahid Abstract A website that encourages and facilities the use […]

February 14th, 2017

Macroeconomic forecasting for Australia using a large number of predictors

Bin Jiang, George Athanasopoulos, Rob J Hyndman, Anastasios Panagiotelis, Farshid Vahid Abstract A popular approach to forecasting macroeconomic variables is […]

January 13th, 2017

Visualising forecasting algorithm performance using time series instance spaces

Yanfei Kang1, Rob J Hyndman2, Kate Smith-Miles3 School of Statistics, Renmin University of China. Department of Econometrics and Business Statistics, […]

May 6th, 2016

Automatic foRecasting using R

Talk given at the Melbourne Data Science Initiative, 6 May 2016.  

February 29th, 2016

On sampling methods for costly multi-objective black-box optimization

Ingrida Steponavičė, Mojdeh Shirazi-Manesh, Rob J. Hyndman, Kate Smith-Miles and Laura Villanova In Advances in Stochastic and Deterministic Global Optimization, […]

February 19th, 2016

Dynamic Algorithm Selection for Pareto Optimal Set Approximation

Ingrida Steponavičė, Rob J Hyndman, Kate Smith-Miles, Laura Villanova Journal of Global Optimization (2016), pp.1-20. Abstract: This paper presents a meta-algorithm […]

February 4th, 2016

Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression

Souhaib BenTaieb, Raphael Huser, Rob J. Hyndman and Marc G. Genton IEEE Transactions on Smart Grid (2016), 7(5), 2448-2455. Abstract: […]

January 25th, 2016

Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond

Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J Hyndman International Journal of Forecasting (2016), 32(3), 896–913. […]

August 17th, 2015

Machine learning bootcamp

A talk on time series forecasting for the Monash University Machine Learning Bootcamp. Demo R code

June 23rd, 2015

MEFM: An R package for long-term probabilistic forecasting of electricity demand

International Symposium on Forecasting Riverside, California   I will describe and demonstrate a new open-source R package that implements the […]

June 19th, 2015

Probabilistic forecasting of peak electricity demand

Southern California Edison Rosemead, California   Electricity demand forecasting plays an important role in short-term load allocation and long-term planning […]

June 4th, 2015

Probabilistic time series forecasting with boosted additive models: an application to smart meter data

By Souhaib Ben Taieb, Raphael Huser, Rob J Hyndman and Marc G Genton