A novel discrete grey seasonal model and its applications

In order to accurately describe real systems with seasonal disturbances, which normally appear monthly or quarterly cycles, a novel discrete grey seasonal model, abbreviated as DGSM(1, 1), is put forward by incorporating the seasonal dummy variables into the conventional model. Moreover, the mechanism and properties of this proposed model are discussed in depth, revealing the inherent differences from the existing seasonal grey models. For validation and explanation purposes, the proposed model is implemented to describe three actual cases with monthly and quarterly seasonal fluctuations (quarterly wind power production, quarterly PM 10, and monthly natural gas consumption), in comparison with five competing models involving grey prediction models, conventional econometric technology, and artificial intelligences. Experimental results from the cases consistently demonstrate that the proposed model significantly outperforms the other benchmark models in terms of several error criteria. Moreover, further discussions about the influences of different sequence lengths on the forecasting performance reveal that the proposed model still performs the best with strong robustness and high reliability in addressing seasonal sequences. In general, the new model is validated to be a powerful and promising methodology for handling sequences with seasonal fluctuations. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A novel discrete grey seasonal model, DGSM(1, 1), is proposed to accurately describe real systems with seasonal disturbances, outperforming other benchmark models on several error criteria. This model demonstrates strong robustness and high reliability in addressing seasonal sequences.