Rob J Hyndman

Souhaib Ben Taieb1 and Rob J Hyndman2 Université Libre de Bruxelles Monash University Abstract: We propose a new forecasting strategy, called rectify, that seeks to combine the best properties of both the recursive and direct forecasting strategies. The rationale behind the rectify strategy is to begin with biased recursive forecasts and adjust them so they are unbiased and have smaller error. We use linear and nonlinear simulated time series to investigate the performance of the rectify strategy and compare the results with those from the recursive and the direct strategies. We also carry out some experiments using real world time

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Andrey V Kostenko and Rob J Hyndman Abstract: Statistical significance testing has little useful purpose in business forecasting, and other tools are to be preferred. For selecting or ranking forecasting methods (especially those based on models) there exist simple but powerful and practical alternative approaches that are not tests in any sense. It is suggested that forecasters place less emphasis on $p$ values and more emphasis on the predictive ability of models. Online article

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 forecast accuracy by simultaneously forecasting a group of products that exhibit similar seasonal demand patterns. Better seasonality estimates can be made by using information on all products in a group, and using these improved estimates when forecasting at the individual product level. This approach is called the group seasonal indices

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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 nonparametric regression. We prove its consistency and asymptotic normality in the interior of the observed data and obtain its rates of convergence. This result is used to obtain practical direct plug-in bandwidth selectors for heteroscedastic regression in one and two dimensions. We show that the local linear estimator with variable

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