关键词: 灰色模型, 商空间, 粒度计算, 构造性机器学习方法, 气象时间序列

Abstract: For the characteristic of meteorological time series data set, under the framework of quotient space granular computing model, complicated meteorological information is analyzed at different layers with different grain-sizes. Additionally, the information is dealt with by the combination method in quotient space theory and the thinking in Multi-Side Increase by Degrees Algorithm. A model that combines grey model GM (1, 1) and structural machine learning method (Alternative Covering Algorithm) is presented for meteorological time series data mining (the forecast of yield). Finally, the experiments on data sets from real world (the yield forecast for winter wheat) are conducted by this model, and the result is satisfying.

Key words: grey model, quotient space, granular computing, structural machine learning method, meteorological time series