The aim of this paper is to present empirical results associated with forecast performance. It is known that common measures of error fail to be scale invariant, and hence cannot be used to make meaningful error comparisons on forecasts across differing time series. This offers a particular challenge toward forecast improvement when one’s intent is to compare error across different units or granularity. Moreover, although it is prudent to test many forecast methods on a time series, one cannot be sure that a single selected method will not lead to complete forecast failure. We address the aforementioned challenges by analyzing a sizable collection of time series in-house.
