Seminar given at Stanford University on 6th October, and University of California (Davis) on 8th October. Related
Workshop for Google, Mountain View, California. Monday 5 October 2015 Automatic algorithms for time series forecasting Optimal forecast reconciliation for […]
Souhaib BenTaieb, Raphael Huser, Rob J. Hyndman and Marc G. Genton Abstract: Smart electricity meters are currently deployed in millions […]
Ingrida Steponavičė, Mojdeh Shirazi-Manesh, Rob J. Hyndman, Kate Smith-Miles and Laura Villanova Abstract We investigate the impact of different sampling […]
A journey from faith via evidence. I was a Christian for nearly 30 years, and was well-known as a writer […]
George Athanasopoulosa, Rob J. Hyndmana, Nikolaos Kourentzesb, Fotios Petropoulosc a Department of Econometrics and Business Statistics, Monash University, Australia b […]
Rob J Hyndman (2015) Editorial, International Journal of Forecasting. Online article. Related
A talk on time series forecasting for the Monash University Machine Learning Bootcamp. Demo R code Related
By Rob J Hyndman, Mohsen B Mesgaran and Roger D Cousens
Christoph Bergmeir1, Rob J Hyndman2, José M Benítez1 Department of Computer Science and Artificial Intelligence, University of Granada, Spain. Department […]
Workshop on Frontiers in Functional Data Analysis Banff, Canada. It is becoming increasingly common for organizations to collect very […]
Yahoo Big Thinkers Sunnyvale, California Friday 26 June 2015, 3:00-4:00 pm Location: Yahoo Sunnyvale Campus and LIVE at labs.yahoo.com […]
Google Mountain View, California. Many applications require a large number of time series to be forecast completely automatically. For […]
International Symposium on Forecasting Riverside, California I will describe and demonstrate a new open-source R package that implements the […]
Southern California Edison Rosemead, California Electricity demand forecasting plays an important role in short-term load allocation and long-term planning […]
Erbas et al.
Journal of Allergy and Clinical Immunology (2015)
By Alex Dokumentov and Rob J Hyndman
By Souhaib Ben Taieb, Raphael Huser, Rob J Hyndman and Marc G Genton
Rob J Hyndman, Earo Wang and Nikolay Laptev
Talk given to a joint meeting of the Statistical Society of Australia (Victorian branch) and the Melbourne Data Science Meetup Group.
The latest version of my talk on electricity demand forecasting.
Given to the “Monash Energy Materials and Systems Institute”
By Christoph Bergmeir, Rob J Hyndman, and Bonsoo Koo
My discussion of the article on “High-dimensional autocovariance matrices and optimal linear prediction”
by Timothy L. McMurry and Dimitris N. Politis.
Electronic J Statistics (2015) 9, 792-796.
Rob J Hyndman (2015) Editorial, International Journal of Forecasting Online article Related
Talk given at the ACEMS Big data workshop, QUT.
Talk given at the Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
By Alexander Dokumentov and Rob J Hyndman
The MEFM package for R includes a set of tools for implementing the Monash Electricity Forecasting Model.
By Rob J Hyndman and George Athanasopoulos
Foresight (Fall, 2014). pp.42-48.
Workshop held at UWA on 23-25 September 2014.
By Rachel Tham, Shyamali Dharmage, Philip Taylor, Ed Newbigin, Mimi L.K. Tang, Don Vicendese, Rob J Hyndman, Michael J Abramson and Bircan Erbas
European Respiratory Journal (2014), 44(Suppl 58).
By Ingrida Steponavičė, Rob J Hyndman, Kate Smith-Miles and Laura Villanova
Learning and Intelligent Optimization.
Lecture Notes in Computer Science, vol 8426, 341-352.
Talk given at the International Symposium on Forecasting, Rotterdam.
A short course given at Humboldt University, Berlin, 24-25 June 2014.
A two-part seminar given at the Energy Forum, Valais/Wallis, Switzerland.
By Alexander Dokumentov and Rob J Hyndman
By Rob J Hyndman, Ala Lee & Earo Wang
By Rob J Hyndman and Farah Yasmeen
Contributions in infinite-dimensional statistics and related topics. Chapter 29, pages 161-166.
The forecast package for R provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
The hts package provides methods for analysing and forecasting hierarchical time series.
By Souhaib Ben Taieb and Rob J Hyndman
International Journal of Forecasting (2014), 30(2), 382–394.
This is a chapter for a new book on forecasting in business.
Talk presented at the conference “New Trends on Intelligent Systems and Soft Computing 2014”, University of Granada, Spain. 13-14 February […]
The demography package for R contains functions for various demographic analyses. It provides facilities for demographic statistics, modelling and forecasting. […]
Souhaib Ben Taieb and Rob J Hyndman International Conference on Machine Learning (ICML) 2014. Abstract Multi-step forecasts can be produced […]
By Heather Booth, Rob J Hyndman and Leonie Tickle. Chapter 8, pages 323-348, Computational Actuarial Science with R Chapman and Hall/CRC […]