Aggregation of Time Series Forecasts via Cacoullos Copula

The simplest linear combination of time series forecasters has shown a better performance than individual models. Thus, many researchers have sought to combine models for improving the forecasting process. This paper introduces a copulas-based approach (CB) for aggregating forecasters. The CB is based on the Cacoullos copula, with architecture divided into three parts: Single Modelling, Marginal Probability Distribution Computational and Joint Probability Distribution Computation. In the first part, the forecasts of individual models are obtained. In the second part, the residuals of the individuals models are calculated. In the third part, the models are combined via Cacoullos copula, based on the obtained residuals. The paper also evaluates the performance of the CB via simulated as well as financial time series (e.g. Google Stock Value). Thus, a comparative analysis is presented between CB and individual models (e.g. Artificial Neural Networks) and alternative combined forecasters (e.g. Simple Average-SA and Normal copula-CN). This study showed that the CB model produces better results when compared with the individual models, SA and CN.