The hybrid grey-based models for temperature prediction.

In this paper several grey-based models are applied to temperature prediction problems. Standard normal distribution, linear regression, and fuzzy techniques are respectively integrated into the grey model to enhance the embedded GM(1, 1), a single variable first order grey model, prediction capability. The original data are preprocessed by the statistical method of standard normal distribution such that they will become normally distributed with a mean of zero and a standard deviation of one. The normalized data are then used to construct the grey model. Due to the inherent error between the predicted and actual outputs, the grey model is further supplemented by either the linear regression or fuzzy method or both to improve the prediction accuracy. Results from predicting the monthly temperatures for two different cities demonstrate that each proposed hybrid methodology can somewhat reduce the prediction errors. When both the statistics and fuzzy methods are incorporated with the grey model, the prediction capability of the hybrid model is quite satisfactory. We repeat the prediction problems in neural networks and the results are also presented for comparison.