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


  Tag: data science

0 posts
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. […]

January 13th, 2016

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, […]

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