John L. Pearce, Madison Hyer, Rob J. Hyndman, Margaret Loughnan, Martine Dennekamp and Neville Nicholls Environmental Health (2016), 15:107 Abstract: […]
Talk given at eRum2016, Poznań, Poland. Related
The smoothAPC package implements smoothing of demographic data. The method uses bivariate thin plate splines, bivariate lasso-type regularization, and allows […]
The stR package implements STR time series decomposition. The methods are described in Dokumentov, A., and Hyndman, R.J. (2016) STR: […]
Han Lin Shang and Rob J Hyndman Journal of Computational and Graphical Statistics (2016) to appear. Abstract Age-specific mortality rates […]
Talk to be given at the German Statistical Week, Augsburg, 15 September 2016 Related
The thief package provides tools for Temporal Hierarchical Forecasting. The methods are described in Forecasting with temporal hierarchies, co-authored with […]
Talk given at the International Symposium on Forecasting. Monday 20 June 2016 Santander, Spain It is common practice to evaluate […]
Rachel Tham, Don Vicendese, Shyamali C Dharmage, Rob J Hyndman, Ed Newbigin, Emma Lewis, Molly O’Sullivan, Adrian J Lowe, Philip […]
Materials for one-day workshop on time series and forecasting presented to ISCRR, Melbourne. Textbook Hyndman and Athanasopoulos (2014): OTexts.org/fpp Software […]
Yanfei Kang1, Rob J Hyndman2, Kate Smith-Miles3 School of Statistics, Renmin University of China. Department of Econometrics and Business Statistics, […]
Talk given at the Melbourne Data Science Initiative, 6 May 2016. Related
Ingrida Steponavičė, Mojdeh Shirazi-Manesh, Rob J. Hyndman, Kate Smith-Miles and Laura Villanova In Advances in Stochastic and Deterministic Global Optimization, […]
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 […]
Souhaib BenTaieb, Raphael Huser, Rob J. Hyndman and Marc G. Genton IEEE Transactions on Smart Grid (2016), 7(5), 2448-2455. Abstract: […]
Bin Jiang, Anastasios Panagiotelis, George Athanasopoulos, Rob J Hyndman, and Farshid Vahid. Department of Econometrics & Business Statistics, Monash University, […]
Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J Hyndman International Journal of Forecasting (2016), 32(3), 896–913. […]
Thomas Url1, Rob J Hyndman2, Alexander Dokumentov2 Vienna University of Economics and Business, Vienna, Austria Monash Business School, Monash University, […]
Christoph Bergmeir1, Rob J Hyndman2, José M Benítez1 Department of Computer Science and Artificial Intelligence, University of Granada, Spain. Department […]
By Rob J Hyndman, Ala Lee & Earo Wang
This is a chapter for a new book on forecasting in business.
Shanika L Wickramasuriya, George Athanasopoulos, Rob J Hyndman Department of Econometrics and Business Statistics, Monash University Abstract Large collections […]
Keynote address given at the Chinese R conference held in Nanchang, Jianxi province. 24-25 October 2015. Related
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 […]
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
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.