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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Automatic time series forecasting

Published on 13 February 2014 in Talks

Talk presented at the conference “New Trends on Intelligent Systems and Soft Computing 2014″, University of Granada, Spain. 13-14 February 2014.

 

Boosting multi-step autoregressive forecasts

Souhaib Ben Taieb and Rob J Hyndman International Conference on Machine Learning (ICML) 2014, to appear. Abstract Multi-step forecasts can be produced recursively by iterating a one-step model, or directly using a specific model for each horizon. Choosing between these two strategies is not an easy task since it involves a trade-off between bias and estimation variance over the forecast horizon. Using a nonlinear machine learning model makes the tradeoff even more difficult. To address this issue, we propose a new forecasting strategy which boosts traditional recursive linear forecasts with a direct strategy using a boosting autoregression procedure at each

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Efficient identification of the Pareto optimal set

Ingrida Steponavičė1, Rob J. Hyndman2, Kate Smith-Miles1 and Laura Villanova3 School of Mathematical Sciences, Monash University, Clayton, Australia Department of Econometrics & Business Statistics, Monash University, Clayton, Australia Ceramic Fuel Cells Limited, Noble Park, Australia Learning and Intelligent OptimizatioN Conference LION 8, Gainesville, Florida – USA, Feb 16-21, 2014 Abstract. In this paper, we focus on expensive multiobjective optimization problems and propose a method to predict an approximation of the Pareto optimal set using classification of sampled decision vectors as dominated or nondominated. The performance of our method, called EPIC, is demonstrated on a set of benchmark problems used in

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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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