The structural Theta method and its predictive performance in the M4-Competition

Abstract

The Theta method is a well-established prediction benchmark widely used in forecast competitions. This method has received significant attention since it was introduced more than 20 years ago, with several authors proposing variants to improve its performance. This paper considers multiple sources of error versions for Theta, belonging to the family of structural time series models. It investigates its out-of-sample forecast performance using the extensive M4-Competition dataset, which includes 100,000 time series. We compare the proposed structural Theta model against several benchmarks, including all variants of the Theta method. The results demonstrate its remarkable predictive abilities as it outperforms all its variants and competitors, emerging as a solid benchmark for use in forecast competitions.