A useful forecast combination benchmark

June 23, 2018

(This article was first published on R on Rob J Hyndman, and kindly contributed to R-bloggers)

Forecasting benchmarks are very important when testing new forecasting methods, to see how well they perform against some simple alternatives. Every week I get sent papers proposing new forecasting methods that fail to do better than even the simplest benchmark. They are rejected without review.
Typical benchmarks include the naïve method (especially for finance and economic data), the seasonal naïve method (for seasonal data), an automatically selected ETS model, and an automatically selected ARIMA model.

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