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Rob J. Hyndman
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2020 – today
- 2024
- [i15]Sevvandi Kandanaarachchi, Conrad Sanderson, Rob J. Hyndman:
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs. CoRR abs/2410.05687 (2024) - 2023
- [j31]Anastasios Panagiotelis
, Puwasala Gamakumara, George Athanasopoulos
Probabilistic forecast reconciliation: Properties, evaluation and score optimisation. Eur. J. Oper. Res. 306(2): 693-706 (2023) - 2022
- [j30]Sayani Gupta, Rob J. Hyndman
Visualizing Probability Distributions Across Bivariate Cyclic Temporal Granularities. J. Comput. Graph. Stat. 31(1): 14-25 (2022) - [j29]Mahsa Ashouri, Rob J. Hyndman
Fast Forecast Reconciliation Using Linear Models. J. Comput. Graph. Stat. 31(1): 263-282 (2022) - [j28]Sevvandi Kandanaarachchi
Leave-One-Out Kernel Density Estimates for Outlier Detection. J. Comput. Graph. Stat. 31(1): 586-599 (2022) - [j27]Mahdi Abolghasemi
Model selection in reconciling hierarchical time series. Mach. Learn. 111(2): 739-789 (2022) - [d64]Rakshitha Godahewa
Extended Wikipedia Web Traffic Daily Dataset (with Missing Values). Zenodo, 2022 - [d63]Rakshitha Godahewa
Extended Wikipedia Web Traffic Daily Dataset (without Missing Values). Zenodo, 2022 - [i14]Sevvandi Kandanaarachchi, Rob J. Hyndman:
Anomaly detection in dynamic networks. CoRR abs/2210.07407 (2022) - 2021
- [j26]Evangelos Spiliotis, Mahdi Abolghasemi
Hierarchical forecast reconciliation with machine learning. Appl. Soft Comput. 112: 107756 (2021) - [j25]Florian Eckert
Forecasting Swiss exports using Bayesian forecast reconciliation. Eur. J. Oper. Res. 291(2): 693-710 (2021) - [j24]Sevvandi Kandanaarachchi
Dimension Reduction for Outlier Detection Using DOBIN. J. Comput. Graph. Stat. 30(1): 204-219 (2021) - [j23]Priyanga Dilini Talagala
Anomaly Detection in High-Dimensional Data. J. Comput. Graph. Stat. 30(2): 360-374 (2021) - [c7]Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Rob J. Hyndman, Pablo Montero-Manso:
Monash Time Series Forecasting Archive. NeurIPS Datasets and Benchmarks 2021 - [d62]Rakshitha Godahewa
Australian Electricity Demand Dataset. Zenodo, 2021 - [d61]Rakshitha Godahewa
COVID-19 Mobility Dataset (with Missing Values). Zenodo, 2021 - [d60]Rakshitha Godahewa
COVID-19 Mobility Dataset (without Missing Values). Zenodo, 2021 - [d59]Rakshitha Godahewa
Bitcoin Dataset with Missing Values. Zenodo, 2021 - [d58]Rakshitha Godahewa
Bitcoin Dataset without Missing Values. Zenodo, 2021 - [d57]Rakshitha Godahewa
Rideshare Dataset with Missing Values. Zenodo, 2021 - [d56]Rakshitha Godahewa
Rideshare Dataset without Missing Values. Zenodo, 2021 - [d55]Rakshitha Godahewa
Vehicle Trips Dataset with Missing Values. Zenodo, 2021 - [d54]Rakshitha Godahewa
Vehicle Trips Dataset without Missing Values. Zenodo, 2021 - [d53]Rakshitha Godahewa
Temperature Rain Dataset with Missing Values. Zenodo, 2021 - [d52]Rakshitha Godahewa
Temperature Rain Dataset without Missing Values. Zenodo, 2021 - [d51]Rakshitha Godahewa
Oikolab Weather Dataset. Zenodo, 2021 - [i13]Fan Cheng, Rob J. Hyndman, Anastasios Panagiotelis:
Manifold learning with approximate nearest neighbors. CoRR abs/2103.11773 (2021) - [i12]Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Rob J. Hyndman, Pablo Montero-Manso:
Monash Time Series Forecasting Archive. CoRR abs/2105.06643 (2021) - [i11]Insha Ullah, Kerrie L. Mengersen, Rob J. Hyndman, James M. McGree:
Detection of cybersecurity attacks through analysis of web browsing activities using principal component analysis. CoRR abs/2107.12592 (2021) - [i10]Hansika Hewamalage, Pablo Montero-Manso, Christoph Bergmeir, Rob J. Hyndman:
A Look at the Evaluation Setup of the M5 Forecasting Competition. CoRR abs/2108.03588 (2021) - [i9]Dilini Rajapaksha, Christoph Bergmeir, Rob J. Hyndman:
LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts. CoRR abs/2111.07001 (2021) - 2020
- [j22]Sevvandi Kandanaarachchi
On normalization and algorithm selection for unsupervised outlier detection. Data Min. Knowl. Discov. 34(2): 309-354 (2020) - [j21]Shanika L. Wickramasuriya
Optimal non-negative forecast reconciliation. Stat. Comput. 30(5): 1167-1182 (2020) - [j20]Yanfei Kang
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics. Stat. Anal. Data Min. 13(4): 354-376 (2020) - [d50]Rakshitha Godahewa
Wind Farms Dataset (without Missing Values). Version 2. Zenodo, 2020 [all versions] - [d49]Rakshitha Godahewa
Wind Farms Dataset (with Missing Values). Version 2. Zenodo, 2020 [all versions] - [d48]Rakshitha Godahewa
Solar Power Dataset (4 Seconds Observations). Version 2. Zenodo, 2020 [all versions] - [d47]Rakshitha Godahewa
Wind Power Dataset (4 Seconds Observations). Version 2. Zenodo, 2020 [all versions] - [d46]Rakshitha Godahewa
Sunspot Daily Dataset (without Missing Values). Version 4. Zenodo, 2020 [all versions] - [d45]Rakshitha Godahewa
Sunspot Daily Dataset (with Missing Values). Version 4. Zenodo, 2020 [all versions] - [d44]Rakshitha Godahewa
M1 Yearly Dataset. Version 4. Zenodo, 2020 [all versions] - [d43]Rakshitha Godahewa
M3 Yearly Dataset. Version 3. Zenodo, 2020 [all versions] - [d42]Rakshitha Godahewa
M3 Quarterly Dataset. Version 3. Zenodo, 2020 [all versions] - [d41]Rakshitha Godahewa
M3 Monthly Dataset. Version 3. Zenodo, 2020 [all versions] - [d40]Rakshitha Godahewa
M3 Other Dataset. Version 3. Zenodo, 2020 [all versions] - [d39]Rakshitha Godahewa
M4 Yearly Dataset. Version 4. Zenodo, 2020 [all versions] - [d38]Rakshitha Godahewa
M4 Quarterly Dataset. Version 3. Zenodo, 2020 [all versions] - [d37]Rakshitha Godahewa
M4 Monthly Dataset. Version 3. Zenodo, 2020 [all versions] - [d36]Rakshitha Godahewa
M4 Weekly Dataset. Version 3. Zenodo, 2020 [all versions] - [d35]Rakshitha Godahewa
M4 Daily Dataset. Version 3. Zenodo, 2020 [all versions] - [d34]Rakshitha Godahewa
London Smart Meters Dataset (without Missing Values). Version 3. Zenodo, 2020 [all versions] - [d33]Rakshitha Godahewa
M4 Hourly Dataset. Version 3. Zenodo, 2020 [all versions] - [d32]Rakshitha Godahewa
Melbourne Pedestrian Counts Dataset. Version 3. Zenodo, 2020 [all versions] - [d31]Rakshitha Godahewa
Kaggle Wikipedia Web Traffic Weekly Dataset. Version 3. Zenodo, 2020 [all versions] - [d30]Rakshitha Godahewa
KDD Cup Dataset (with Missing Values). Version 4. Zenodo, 2020 [all versions] - [d29]Rakshitha Godahewa
KDD Cup Dataset (without Missing Values). Version 4. Zenodo, 2020 [all versions] - [d28]Rakshitha Godahewa
COVID-19 Deaths Dataset. Version 2. Zenodo, 2020 [all versions] - [d27]Rakshitha Godahewa
Hospital Dataset. Version 2. Zenodo, 2020 [all versions] - [d26]Rakshitha Godahewa
Car Parts Dataset (without Missing Values). Version 2. Zenodo, 2020 [all versions] - [d25]Rakshitha Godahewa
Car Parts Dataset (with Missing Values). Version 2. Zenodo, 2020 [all versions] - [d24]Rakshitha Godahewa
CIF 2016 Dataset. Version 5. Zenodo, 2020 [all versions] - [d23]Rakshitha Godahewa
US Births Dataset. Version 2. Zenodo, 2020 [all versions] - [d22]Rakshitha Godahewa
Saugeen River Flow (SaugeenDay) Dataset. Version 2. Zenodo, 2020 [all versions] - [d21]Rakshitha Godahewa
Electricity Demand (Elecdemand) Dataset. Version 2. Zenodo, 2020 [all versions] - [d20]Rakshitha Godahewa
London Smart Meters Dataset (with Missing Values). Version 3. Zenodo, 2020 [all versions] - [d19]Rakshitha Godahewa
Kaggle Wikipedia Web Traffic Daily Dataset (without Missing Values). Version 3. Zenodo, 2020 [all versions] - [d18]Rakshitha Godahewa
Kaggle Wikipedia Web Traffic Daily Dataset (with Missing Values). Version 3. Zenodo, 2020 [all versions] - [d17]Rakshitha Godahewa
Tourism Quarterly Dataset. Version 3. Zenodo, 2020 [all versions] - [d16]Rakshitha Godahewa
Tourism Monthly Dataset. Version 3. Zenodo, 2020 [all versions] - [d15]Rakshitha Godahewa
Tourism Yearly Dataset. Version 3. Zenodo, 2020 [all versions] - [d14]Rakshitha Godahewa
NN5 Daily Dataset (with Missing Values). Version 3. Zenodo, 2020 [all versions] - [d13]Rakshitha Godahewa
NN5 Daily Dataset (without Missing Values). Version 3. Zenodo, 2020 [all versions] - [d12]Rakshitha Godahewa
NN5 Weekly Dataset. Version 3. Zenodo, 2020 [all versions] - [d11]Rakshitha Godahewa
Traffic Hourly Dataset. Version 3. Zenodo, 2020 [all versions] - [d10]Rakshitha Godahewa
Traffic Weekly Dataset. Version 3. Zenodo, 2020 [all versions] - [d9]Rakshitha Godahewa
Electricity Hourly Dataset. Version 3. Zenodo, 2020 [all versions] - [d8]Rakshitha Godahewa
Electricity Weekly Dataset. Version 3. Zenodo, 2020 [all versions] - [d7]Rakshitha Godahewa
Solar Dataset (10 Minutes Observations). Version 3. Zenodo, 2020 [all versions] - [d6]Rakshitha Godahewa
Solar Weekly Dataset. Version 3. Zenodo, 2020 [all versions] - [d5]Rakshitha Godahewa
M1 Quarterly Dataset. Version 3. Zenodo, 2020 [all versions] - [d4]Rakshitha Godahewa
M1 Monthly Dataset. Version 3. Zenodo, 2020 [all versions] - [d3]Rakshitha Godahewa
Dominick Dataset. Version 2. Zenodo, 2020 [all versions] - [d2]Rakshitha Godahewa
Weather Dataset. Version 2. Zenodo, 2020 [all versions] - [d1]Rakshitha Godahewa
FRED-MD Dataset. Version 2. Zenodo, 2020 [all versions] - [i8]Evangelos Spiliotis, Mahdi Abolghasemi, Rob J. Hyndman, Fotios Petropoulos, Vassilios Assimakopoulos:
Hierarchical forecast reconciliation with machine learning. CoRR abs/2006.02043 (2020) - [i7]Pablo Montero-Manso, Rob J. Hyndman:
Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality. CoRR abs/2008.00444 (2020) - [i6]Bahman Rostami Tabar, Mohammad M. Ali, Tao Hong, Rob J. Hyndman, Michael D. Porter, Aris A. Syntetos:
Forecasting for Social Good. CoRR abs/2009.11669 (2020) - [i5]Mahdi Abolghasemi, Rob J. Hyndman, Evangelos Spiliotis, Christoph Bergmeir:
Model selection in reconciling hierarchical time series. CoRR abs/2010.10742 (2020)
2010 – 2019
- 2019
- [i4]Yanfei Kang, Rob J. Hyndman, Feng Li:
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics. CoRR abs/1903.02787 (2019) - [i3]Priyanga Dilini Talagala, Rob J. Hyndman, Kate Smith-Miles:
Anomaly Detection in High Dimensional Data. CoRR abs/1908.04000 (2019) - [i2]Mahdi Abolghasemi, Rob J. Hyndman, Garth Tarr, Christoph Bergmeir:
Machine learning applications in time series hierarchical forecasting. CoRR abs/1912.00370 (2019) - 2018
- [j19]Christoph Bergmeir
A note on the validity of cross-validation for evaluating autoregressive time series prediction. Comput. Stat. Data Anal. 120: 70-83 (2018) - [j18]Fotios Petropoulos
Exploring the sources of uncertainty: Why does bagging for time series forecasting work? Eur. J. Oper. Res. 268(2): 545-554 (2018) - [i1]Anne Driemel, Rob J. Hyndman, Galit Shmueli:
Analysing Large Collections of Time Series (NII Shonan Meeting 2018-3). NII Shonan Meet. Rep. 2018 (2018) - 2017
- [j17]George Athanasopoulos
Forecasting with temporal hierarchies. Eur. J. Oper. Res. 262(1): 60-74 (2017) - [j16]Ingrida Steponavice, Rob J. Hyndman
Dynamic algorithm selection for pareto optimal set approximation. J. Glob. Optim. 67(1-2): 263-282 (2017) - [j15]Paul Goodwin, Fotios Petropoulos
A note on upper bounds for forecast-value-added relative to naïve forecasts. J. Oper. Res. Soc. 68(9): 1082-1084 (2017) - [c6]Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman:
Coherent Probabilistic Forecasts for Hierarchical Time Series. ICML 2017: 3348-3357 - 2016
- [j14]Rob J. Hyndman
Fast computation of reconciled forecasts for hierarchical and grouped time series. Comput. Stat. Data Anal. 97: 16-32 (2016) - [j13]Souhaib Ben Taieb
Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression. IEEE Trans. Smart Grid 7(5): 2448-2455 (2016) - [p1]Ingrida Steponavice, Mojdeh Shirazi-Manesh, Rob J. Hyndman
On Sampling Methods for Costly Multi-Objective Black-Box Optimization. Advances in Stochastic and Deterministic Global Optimization 2016: 273-296 - 2015
- [c5]Rob J. Hyndman
Large-Scale Unusual Time Series Detection. ICDM Workshops 2015: 1616-1619 - 2014
- [c4]Souhaib Ben Taieb, Rob J. Hyndman:
Boosting multi-step autoregressive forecasts. ICML 2014: 109-117 - [c3]Ingrida Steponavice, Rob J. Hyndman
Efficient Identification of the Pareto Optimal Set. LION 2014: 341-352 - 2011
- [j12]Jae H. Kim, Iain Fraser
Improved interval estimation of long run response from a dynamic linear model: A highest density region approach. Comput. Stat. Data Anal. 55(8): 2477-2489 (2011) - [j11]Rob J. Hyndman
Optimal combination forecasts for hierarchical time series. Comput. Stat. Data Anal. 55(9): 2579-2589 (2011) - [j10]Han Lin Shang
Nonparametric time series forecasting with dynamic updating. Math. Comput. Simul. 81(7): 1310-1324 (2011) - [r3]Rob J. Hyndman:
Business Forecasting Methods. International Encyclopedia of Statistical Science 2011: 185-187 - [r2]Rob J. Hyndman:
Forecasting: An Overview. International Encyclopedia of Statistical Science 2011: 536-539 - [r1]Rob J. Hyndman:
Moving Averages. International Encyclopedia of Statistical Science 2011: 866-869 - 2010
- [c2]Laura Villanova
Functionalization of microarray devices: Process optimization using a multiobjective PSO and multiresponse MARS modeling. IEEE Congress on Evolutionary Computation 2010: 1-8
2000 – 2009
- 2009
- [j9]Xiaozhe Wang, Kate Amanda Smith-Miles
Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series. Neurocomputing 72(10-12): 2581-2594 (2009) - 2008
- [j8]Phillip G. Gould, Anne B. Koehler, J. Keith Ord, Ralph D. Snyder
Forecasting time series with multiple seasonal patterns. Eur. J. Oper. Res. 191(1): 207-222 (2008) - 2007
- [j7]Jae H. Kim, Param Silvapulle, Rob J. Hyndman
Half-life estimation based on the bias-corrected bootstrap: A highest density region approach. Comput. Stat. Data Anal. 51(7): 3418-3432 (2007) - [j6]Rob J. Hyndman
Robust forecasting of mortality and fertility rates: A functional data approach. Comput. Stat. Data Anal. 51(10): 4942-4956 (2007) - 2006
- [j5]Xibin Zhang, Maxwell L. King, Rob J. Hyndman
A Bayesian approach to bandwidth selection for multivariate kernel density estimation. Comput. Stat. Data Anal. 50(11): 3009-3031 (2006) - [j4]Xiaozhe Wang, Kate A. Smith
Characteristic-Based Clustering for Time Series Data. Data Min. Knowl. Discov. 13(3): 335-364 (2006) - [j3]A. V. Kostenko, Rob J. Hyndman
A note on the categorization of demand patterns. J. Oper. Res. Soc. 57(10): 1256-1257 (2006) - [j2]Denny Meyer, Rob J. Hyndman:
The accuracy of television network rating forecasts: The effects of data aggregation and alternative models. Model. Assist. Stat. Appl. 1(3): 147-155 (2006) - 2005
- [c1]Xiaozhe Wang, Kate A. Smith, Rob J. Hyndman
Dimension Reduction for Clustering Time Series Using Global Characteristics. International Conference on Computational Science (3) 2005: 792-795 - 2004
- [j1]Ralph D. Snyder
Exponential smoothing models: Means and variances for lead-time demand. Eur. J. Oper. Res. 158(2): 444-455 (2004)
Coauthor Index
[j27] [d64] [d63] [c7] [d62] [d61] [d60] [d59] [d58] [d57] [d56] [d55] [d54] [d53] [d52] [d51] [i12] [i10] [i9] [d50] [d49] [d48] [d47] [d46] [d45] [d44] [d43] [d42] [d41] [d40] [d39] [d38] [d37] [d36] [d35] [d34] [d33] [d32] [d31] [d30] [d29] [d28] [d27] [d26] [d25] [d24] [d23] [d22] [d21] [d20] [d19] [d18] [d17] [d16] [d15] [d14] [d13] [d12] [d11] [d10] [d9] [d8] [d7] [d6] [d5] [d4] [d3] [d2] [d1] [i5] [i2] [j19] [j18]
[d64] [d63] [c7] [d62] [d61] [d60] [d59] [d58] [d57] [d56] [d55] [d54] [d53] [d52] [d51] [i12] [d50] [d49] [d48] [d47] [d46] [d45] [d44] [d43] [d42] [d41] [d40] [d39] [d38] [d37] [d36] [d35] [d34] [d33] [d32] [d31] [d30] [d29] [d28] [d27] [d26] [d25] [d24] [d23] [d22] [d21] [d20] [d19] [d18] [d17] [d16] [d15] [d14] [d13] [d12] [d11] [d10] [d9] [d8] [d7] [d6] [d5] [d4] [d3] [d2] [d1]
[d64] [d63] [c7] [d62] [d61] [d60] [d59] [d58] [d57] [d56] [d55] [d54] [d53] [d52] [d51] [i12] [i10] [d50] [d49] [d48] [d47] [d46] [d45] [d44] [d43] [d42] [d41] [d40] [d39] [d38] [d37] [d36] [d35] [d34] [d33] [d32] [d31] [d30] [d29] [d28] [d27] [d26] [d25] [d24] [d23] [d22] [d21] [d20] [d19] [d18] [d17] [d16] [d15] [d14] [d13] [d12] [d11] [d10] [d9] [d8] [d7] [d6] [d5] [d4] [d3] [d2] [d1] [i7]
Geoffrey I. Webb
aka: Geoff Webb
aka: Geoff Webb
[d64] [d63] [c7] [d62] [d61] [d60] [d59] [d58] [d57] [d56] [d55] [d54] [d53] [d52] [d51] [i12] [d50] [d49] [d48] [d47] [d46] [d45] [d44] [d43] [d42] [d41] [d40] [d39] [d38] [d37] [d36] [d35] [d34] [d33] [d32] [d31] [d30] [d29] [d28] [d27] [d26] [d25] [d24] [d23] [d22] [d21] [d20] [d19] [d18] [d17] [d16] [d15] [d14] [d13] [d12] [d11] [d10] [d9] [d8] [d7] [d6] [d5] [d4] [d3] [d2] [d1]
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