“…For time series prediction, GM(1,1) is among the most frequently used grey prediction models [10]. It requires only four recent samples to derive reliable and acceptable prediction accuracy [5], and has been widely applied to various decision problems involving management, economics, and engineering [2][3][4][11][12][13][14][15][16]. To better improve the prediction performance of the original GM(1,1) model, several versions combining with computational intelligence have been proposed, such as models with self-adaptive intelligence [17], neural-network-based grey prediction for electricity consumption prediction [18,19], PGM(1,1) using particle swarm optimization to determine the development coefficient [20], GM(1,1) models with online sequential extreme learning machine [21], an optimized nonlinear grey Bernoulli model [22], an adaptive GM(1,1) for electricity consumption [3], and grey wave forecasting through qualified contour sequences [23].…”