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Improving Automated Time Series Forecasting with the use of Model Ensembles


There currently exist several “black box” software libraries for the automatic forecasting of time series. Popular among these are the 'forecast' and 'bsts' packages for R, which have

functions to automatically fit several common classes of time series models, such as the

autoregressive integrated moving average (ARIMA) and the family of exponential smoothing

models, among others. It is often the case that what one gains from the ease in fitting these

automatic methods comes at the cost of predictive performance. In this paper, we propose

several methods to improve the prediction accuracy of automatic time series forecasting, all of

which relate to creating ensembles of models automatically fit from these packages. We

explore different ways that one can construct these ensembles and evaluate each on a

benchmark time series dataset. In addition, we provide the R code used to construct these ensembles.

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