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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