The grey theory can be applied in the research of prediction, decision-making and control, especially in prediction. However, traditional grey models show some limitations which affect to the model applicability and the prediction accuracy. So, this paper proposes a novel comprehensive adaptive grey model CAGM(1,N) in order to overcome the disadvantages existing in the traditional GM(1,N). The proposed model is developed from GM(1,N) model with three improvements. The first one is a use of two smartly additive factors to convert any raw data into a grey sequence which satisfies both the raw data checking condition and quasi-smooth condition to perform the grey estimation. The second one is a modification in calculating the background series which affect to the grey model accuracy. And the final improvement is a modification in computing the model output by using error correcting accumulation to eliminate the residual prediction errors. The CAGM(1,N) model can be applied to any practical prediction problem and obtain higher fitting and prediction accuracy comparing with the traditional GM(1,N) model. The case study has been carried out to certify the improved model.
