Rob J. Hyndman

Orcid: 0000-0002-2140-5352

According to our database¹, Rob J. Hyndman authored at least 51 papers between 2004 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

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Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2023

Probabilistic forecast reconciliation: Properties, evaluation and score optimisation.

[BibT_eX]

[DOI]

Anastasios Panagiotelis

Puwasala Gamakumara

George Athanasopoulos

Rob J. Hyndman

Eur. J. Oper. Res., 2023

2022

Leave-One-Out Kernel Density Estimates for Outlier Detection.

[BibT_eX]

[DOI]

Sevvandi Kandanaarachchi

Rob J. Hyndman

J. Comput. Graph. Stat., January, 2022

Model selection in reconciling hierarchical time series.

[BibT_eX]

[DOI]

Mach. Learn., 2022

Visualizing Probability Distributions Across Bivariate Cyclic Temporal Granularities.

[BibT_eX]

[DOI]

J. Comput. Graph. Stat., 2022

Fast Forecast Reconciliation Using Linear Models.

[BibT_eX]

[DOI]

Mahsa Ashouri

Rob J. Hyndman

Galit Shmueli

J. Comput. Graph. Stat., 2022

Anomaly detection in dynamic networks.

[BibT_eX]

[DOI]

Sevvandi Kandanaarachchi

Rob J. Hyndman

CoRR, 2022

2021

Anomaly Detection in High-Dimensional Data.

[BibT_eX]

[DOI]

Priyanga Dilini Talagala

Rob J. Hyndman

Kate Smith-Miles

J. Comput. Graph. Stat., 2021

Dimension Reduction for Outlier Detection Using DOBIN.

[BibT_eX]

[DOI]

Sevvandi Kandanaarachchi

Rob J. Hyndman

J. Comput. Graph. Stat., 2021

Forecasting Swiss exports using Bayesian forecast reconciliation.

[BibT_eX]

[DOI]

Florian Eckert

Rob J. Hyndman

Anastasios Panagiotelis

Eur. J. Oper. Res., 2021

LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts.

[BibT_eX]

[DOI]

Dilini Rajapaksha

Christoph Bergmeir

Rob J. Hyndman

CoRR, 2021

A Look at the Evaluation Setup of the M5 Forecasting Competition.

[BibT_eX]

[DOI]

CoRR, 2021

Detection of cybersecurity attacks through analysis of web browsing activities using principal component analysis.

[BibT_eX]

[DOI]

CoRR, 2021

Manifold learning with approximate nearest neighbors.

[BibT_eX]

[DOI]

Fan Cheng

Rob J. Hyndman

Anastasios Panagiotelis

CoRR, 2021

Hierarchical forecast reconciliation with machine learning.

[BibT_eX]

[DOI]

Vassilios Assimakopoulos

Appl. Soft Comput., 2021

Monash Time Series Forecasting Archive.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

GRATIS: GeneRAting TIme Series with diverse and controllable characteristics.

[BibT_eX]

[DOI]

Yanfei Kang

Rob J. Hyndman

Feng Li

Stat. Anal. Data Min., 2020

Optimal non-negative forecast reconciliation.

[BibT_eX]

[DOI]

Shanika L. Wickramasuriya

Berwin A. Turlach

Rob J. Hyndman

Stat. Comput., 2020

On normalization and algorithm selection for unsupervised outlier detection.

[BibT_eX]

[DOI]

Sevvandi Kandanaarachchi

Mario A. Muñoz

Rob J. Hyndman

Kate Smith-Miles

Data Min. Knowl. Discov., 2020

Forecasting for Social Good.

[BibT_eX]

[DOI]

CoRR, 2020

Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality.

[BibT_eX]

[DOI]

Pablo Montero-Manso

Rob J. Hyndman

CoRR, 2020

2019

Machine learning applications in time series hierarchical forecasting.

[BibT_eX]

[DOI]

CoRR, 2019

2018

Analysing Large Collections of Time Series (NII Shonan Meeting 2018-3).

[BibT_eX]

[DOI]

Anne Driemel

Rob J. Hyndman

Galit Shmueli

NII Shonan Meet. Rep., 2018

Exploring the sources of uncertainty: Why does bagging for time series forecasting work?

[BibT_eX]

[DOI]

Fotios Petropoulos

Rob J. Hyndman

Christoph Bergmeir

Eur. J. Oper. Res., 2018

A note on the validity of cross-validation for evaluating autoregressive time series prediction.

[BibT_eX]

[DOI]

Christoph Bergmeir

Rob J. Hyndman

Bonsoo Koo

Comput. Stat. Data Anal., 2018

2017

A note on upper bounds for forecast-value-added relative to naïve forecasts.

[BibT_eX]

[DOI]

Paul Goodwin

Fotios Petropoulos

Rob J. Hyndman

J. Oper. Res. Soc., 2017

Dynamic algorithm selection for pareto optimal set approximation.

[BibT_eX]

[DOI]

J. Glob. Optim., 2017

Forecasting with temporal hierarchies.

[BibT_eX]

[DOI]

George Athanasopoulos

Rob J. Hyndman

Nikolaos Kourentzes

Fotios Petropoulos

Eur. J. Oper. Res., 2017

Coherent Probabilistic Forecasts for Hierarchical Time Series.

[BibT_eX]

[DOI]

Souhaib Ben Taieb

James W. Taylor

Rob J. Hyndman

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression.

[BibT_eX]

[DOI]

IEEE Trans. Smart Grid, 2016

Fast computation of reconciled forecasts for hierarchical and grouped time series.

[BibT_eX]

[DOI]

Rob J. Hyndman

Alan J. Lee

Earo Wang

Comput. Stat. Data Anal., 2016

On Sampling Methods for Costly Multi-Objective Black-Box Optimization.

[BibT_eX]

[DOI]

Ingrida Steponavice

Mojdeh Shirazi-Manesh

Rob J. Hyndman

Kate Smith-Miles

Laura Villanova

Proceedings of the Advances in Stochastic and Deterministic Global Optimization., 2016

2015

Large-Scale Unusual Time Series Detection.

[BibT_eX]

[DOI]

Rob J. Hyndman

Earo Wang

Nikolay Laptev

Proceedings of the IEEE International Conference on Data Mining Workshop, 2015

2014

Efficient Identification of the Pareto Optimal Set.

[BibT_eX]

[DOI]

Proceedings of the Learning and Intelligent Optimization, 2014

Boosting multi-step autoregressive forecasts.

[BibT_eX]

[DOI]

Souhaib Ben Taieb

Rob J. Hyndman

Proceedings of the 31th International Conference on Machine Learning, 2014

2011

Moving Averages.

[BibT_eX]

[DOI]

Rob J. Hyndman

Proceedings of the International Encyclopedia of Statistical Science, 2011

Forecasting: An Overview.

[BibT_eX]

[DOI]

Rob J. Hyndman

Proceedings of the International Encyclopedia of Statistical Science, 2011

Business Forecasting Methods.

[BibT_eX]

[DOI]

Rob J. Hyndman

Proceedings of the International Encyclopedia of Statistical Science, 2011

Nonparametric time series forecasting with dynamic updating.

[BibT_eX]

[DOI]

Han Lin Shang

Rob J. Hyndman

Math. Comput. Simul., 2011

Improved interval estimation of long run response from a dynamic linear model: A highest density region approach.

[BibT_eX]

[DOI]

Jae H. Kim

Iain Fraser

Rob J. Hyndman

Comput. Stat. Data Anal., 2011

Optimal combination forecasts for hierarchical time series.

[BibT_eX]

[DOI]

Rob J. Hyndman

Roman A. Ahmed

George Athanasopoulos

Han Lin Shang

Comput. Stat. Data Anal., 2011

2010

Functionalization of microarray devices: Process optimization using a multiobjective PSO and multiresponse MARS modeling.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2010

2009

Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series.

[BibT_eX]

[DOI]

Xiaozhe Wang

Kate Amanda Smith-Miles

Rob J. Hyndman

Neurocomputing, 2009

2008

Forecasting time series with multiple seasonal patterns.

[BibT_eX]

[DOI]

Eur. J. Oper. Res., 2008

2007

Half-life estimation based on the bias-corrected bootstrap: A highest density region approach.

[BibT_eX]

[DOI]

Jae H. Kim

Param Silvapulle

Rob J. Hyndman

Comput. Stat. Data Anal., 2007

Robust forecasting of mortality and fertility rates: A functional data approach.

[BibT_eX]

[DOI]

Rob J. Hyndman

Md. Shahid Ullah

Comput. Stat. Data Anal., 2007

2006

The accuracy of television network rating forecasts: The effects of data aggregation and alternative models.

[BibT_eX]

[DOI]

Denny Meyer

Rob J. Hyndman

Model. Assist. Stat. Appl., 2006

A note on the categorization of demand patterns.

[BibT_eX]

[DOI]

A. V. Kostenko

Rob J. Hyndman

J. Oper. Res. Soc., 2006

Characteristic-Based Clustering for Time Series Data.

[BibT_eX]

[DOI]

Xiaozhe Wang

Kate A. Smith

Rob J. Hyndman

Data Min. Knowl. Discov., 2006

A Bayesian approach to bandwidth selection for multivariate kernel density estimation.

[BibT_eX]

[DOI]

Xibin Zhang

Maxwell L. King

Rob J. Hyndman

Comput. Stat. Data Anal., 2006

2005

Dimension Reduction for Clustering Time Series Using Global Characteristics.

[BibT_eX]

[DOI]

Xiaozhe Wang

Kate A. Smith

Rob J. Hyndman

Proceedings of the Computational Science, 2005

2004

Exponential smoothing models: Means and variances for lead-time demand.

[BibT_eX]

[DOI]

Eur. J. Oper. Res., 2004

Rob J. Hyndman

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