Keywords: Time Series, Forecasting, Robust Statistics, Exponential Smoothing
Webpages:
https://CRAN.R-project.org/package=robets,
https://rcrevits.wordpress.com/ Simple forecasting methods, such as exponential smoothing, are very popular in business analytics. This is not only due to their simplicity, but also because they perform very well, in particular for shorter time series. Incorporating trend and seasonality into an exponential smoothing method is standard. Many real time series, show seasonal patterns that should be exploited for forecasting purposes. Including a trend or not may be less clear. For instance, weekly sales (in units) may show an increasing trend, but the sales will not grow to infinity. Here, the damped trend model gives an outcome. Damped trend exponential smoothing gives excellent results in forecasting competitions.
In a highly cited paper, Hyndman and Khandakar (2008) developed an automatic forecasting method using exponential smoothing, available as the
R package
forecast. We propose the package
robets, an outlier robust alternative of the function ets in the
forecast package. For each method of a class of exponential smoothing variants we made a robust alternative. The class includes methods with a damped trend and/or seasonal components. The robust method is developed by robustifying every aspect of the original exponential smoothing variant. We provide robust forecasting equations, robust initial values, robust smoothing parameter estimation and a robust information criterion. The method is an extension of Gelper, Fried, and Croux (2010) and is described in more detail in Crevits and Croux (2016).
The code of the developed
R package is based on the function ets of the
forecast package. The usual functions for visualizing the models and forecasts also work for robets objects. Additionally there is a function plotOutliers which highlights outlying values in a time series.
References Crevits, Ruben, and Christophe Croux. 2016. “Forecasting with Robust Exponential Smoothing with Damped Trend and Seasonal Components.”
Working Paper.
Gelper, S, R Fried, and C Croux. 2010. “Robust Forecasting with Exponential and Holt-Winters Smoothing.”
Journal of Forecasting 29: 285–300.
Hyndman, R J, and Y Khandakar. 2008. “Automatic Time Series Forecasting: The Forecast Package for R.”
Journal of Statistical Software 27 (3).
