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on Forecasting |
| By: | Ruben Crevits; Christophe Croux |
| Abstract: | We provide a framework for robust exponential smoothing. For a class of exponential smoothing variants, we present a robust alternative. The class includes models with a damped trend and/or seasonal components. We provide robust forecasting equations, robust starting values, robust smoothing parameter estimation and a robust information criterion. The method is implemented in the R package robets, allowing for automatic forecasting. We compare the standard non-robust version with the robust alternative in a simulation study. Finally, the methodology is tested on data. |
| Keywords: | Automatic Forecasting, Outliers, R package, Time series |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:ete:kbiper:588812&r=for |
| By: | Michael B. Gordy; Hsiao Yen Lok; Alexander J. McNeil |
| Abstract: | We study a class of backtests for forecast distributions in which the test statistic is a spectral transformation that weights exceedance events by a function of the modeled probability level. The choice of the kernel function makes explicit the user's priorities for model performance. The class of spectral backtests includes tests of unconditional coverage and tests of conditional coverage. We show how the class embeds a wide variety of backtests in the existing literature, and propose novel variants as well. We assess the size and power of the backtests in realistic sample sizes, and in particular demonstrate the tradeoff between power and specificity in validating quantile forecasts. |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1708.01489&r=for |