Combining Time Series Forecasting Models via Gumbel-Hougaard Copulas

Researchers have been challenged to combine time series forecasting models, with the intention of enhancing forecast accuracy and efficiency. In this way, to weight models accuracy, efficiency, and mutual dependency becomes paramount. A promising way to address this issue is via copulas. Copulas are joint probability distribution functions aimed to envelop both the marginal distribution as well as the dependency among variables (e:g: forecasting models). This paper introduces copulas in the problem of combining time series forecasting models and proposes a maximum likelihood-based methodology in this context. Specifically, a Gumbel-Hougaard copulas model is presented. The usefulness of the resulting methodology is illustrated by means of simulated cases involving the combination of two single ARIMA models.