Archive for the ‘Working papers’ Category:


Bagging Exponential Smoothing Methods using STL Decomposition and Box-Cox Transformation

Published on 21 March 2014 in Working papers

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 methods. The bagging uses a Box-Cox transformation followed by an STL decomposition to separate the time series into trend, seasonal part, and remainder. The remainder is then bootstrapped using a moving block bootstrap, and a new series is assembled using this bootstrapped remainder. On the bootstrapped series, an ensemble of

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Two-dimensional smoothing of mortality rates

Alexander Dokumentov and Rob J Hyndman Abstract: We propose three new practical methods of smoothing mortality rates (the procedure known in demography as graduation) over two dimensions: age and time. The first method uses bivariate thin plate splines. The second uses a similar procedure but with lasso-type regularization. The third method also uses bivariate lasso-type regularization, but allows for both period and cohort effects. Thus the mortality rates are modelled as the sum of four components: a smooth bivariate function of age and time, smooth one-dimensional cohort effects, smooth one-dimensional period effects and random errors. Cross validation is used to compare these new methods of graduation with

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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 input-output relationships, costly or time-consuming experimental studies and irregular experimental regions (e.g., non-cubic or non-spherical regions induced by constraints in the input variables). Such developments may involve many different research areas simultaneously (e.g., the statistical design of the experiments, multivariate modeling, and multi-objective optimization). Experts in various research fields have

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hts: An R package for forecasting hierarchical or grouped time series

Rob J Hyndman, George Athanasopoulos and Han Lin Shang The new version of the hts package (v3.01) has a vignette.

 

Recursive and direct multi-step forecasting: the best of both worlds

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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