Low-dimensional decomposition, smoothing and forecasting of sparse functional data

Alexander Dokumentov and Rob J Hyndman Department of Econometrics & Business Statistics, Monash University, Australia Abstract: We propose a new generic method ROPES (Regularized Optimization for Prediction and Estimation with Sparse data) for decomposing, smoothing and forecasting two-dimensional sparse data. In some ways, ROPES is similar to Ridge Regression, the

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Fast computation of reconciled forecasts for hierarchical and grouped time series

Rob J Hyndman1, Alan Lee2 & Earo Wang1 Department of Econometrics and Business Statistics, Monash University, Australia University of Auckland, New Zealand. Abstract We describe some fast algorithms for reconciling large collections of time series forecasts with aggregation constraints. The constraints arise due to the need for forecasts of collections

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“Facts” may still be artefacts, since models can make unrealistic assumptions: statistical methods for the estimation of invasion lag-phases from herbarium data

Rob J. Hyndman1, Mohsen B. Mesgaran2 and Roger D. Cousens2 Department of Econometrics & Business Statistics, Monash University, VIC 3800, Australia Department of Resource Management & Geography, The University of Melbourne, Victoria 3010, Australia Abstract We present a new method for estimating the length of the invasion lag phase from

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Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation

Christoph Bergmeir1, Rob J Hyndman2, José M Benítez1 Department of Computer Science and Artificial Intelligence, University of Granada, Spain. Department of Econometrics and Business Statistics, Monash University, Australia. Abstract: Exponential smoothing is one of the most popular forecasting methods. We present a method for bootstrap aggregation (bagging) of exponential smoothing

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Nonparametric and semiparametric response surface methodology: a review of designs, models and optimization techniques

Laura Villanova, Rob J Hyndman, Kate Smith-Miles, Irene Poli Abstract: Since the introduction of Response Surface Methodology in the 1950s, there have been many developments with the aim of expanding the range of applications of the methodology. Various new design, modeling and optimization techniques have been introduced for coping with unknown

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A state space model for exponential smoothing with group seasonality

Pim Ouwehand1 , Rob J Hyndman2 , Ton G. de Kok1 and Karel H. van Donselaar1 Department of Technology Management, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Abstract We present an approach to improve

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Local linear multivariate regression with variable bandwidth in the presence of heteroscedasticity

Azhong Ye1 , Rob J Hyndman2 and Zinai Li3 College of Management, Fuzhou University, Fuzhou, 350002, China. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. School of Economics and Management, Tsinghua University, Beijing, 100084, China. Abstract We present a local linear estimator with variable bandwidth for multivariate

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Time series forecasting: the case for the single source of error state space approach

J. Keith Ord1 , Ralph D. Snyder2 , Anne B. Koehler3 , Rob J. Hyndman2 and Mark Leeds4 320 Old North, Georgetown University, Washington, DC 20057, USA. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Department of Decision Sciences and Management Information Systems, Miami University, Oxford, OH

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Seasonal adjustment methods for the analysis of respiratory disease in environmental epidemiology

Bircan Erbas1 and Rob J Hyndman2 Department of General Practice & Public Health, The University of Melbourne, VIC 3010, Australia. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Abstract We study the relationship between daily hospital admissions for respiratory disease and various pollutant and climatic variables, looking

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A unified view of linear AR(1) models

Grunwald, G.K., Hyndman, R.J., and Tedesco, L. Abstract We review and synthesize the wide range of non-Gaussian first order linear autoregressive models that have appeared in the literature. Models are organized into broad classes to clarify similarities and differences and facilitate application in particular situations. General properties for process mean,

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