基于改进灰色马尔科夫模型的年降水量预测

Prediction of Annual Precipitation Based on Improved Grey Markov Model

通过结合灰色预测和马尔科夫理论的特点,利用新信息优先的思想,提出一种改进的灰色马尔科夫预测模型,首先对序列进行滑动平均处理,然后用无偏GM(1,1)模型拟合系统的发展变化趋势,并以此为基础进行马尔科夫预测,在每一步预测中,不断推陈出新,更新原始数据.实验结果表明,与一般的灰色预测模型相比,其预测准确度尤其是中长期预测准确度得到了较大提高.

By combining the advantages of both grey prediction and Markov theory, an improved grey-Markov model is established, first the sequence is moved average, and then unbiased grey-Markov model is established to imitate the development tendency of the forecast system while Markov prediction is used to forecast the fluctuation along the tendency. The newest data is gradually added while the oldest one is removed from original data sequence. Experiment results show that the prediction accuracy has been improved quite a lot in comparison with the grey-Markov model used for middle and long term prediction of system objects.