关键词: 时间序列, 集成学习, 分类, 顺序子空间, 堆叠泛化

Abstract:

 To solve the early classification for time series in some time-sensitive application area, an ensemble learning method named sequential subspace stacked generation (SSSG) was introduced. This method split time series into several sequential subspaces with slider windows. Multi first-layer classifiers were used on these sequential subspaces and label probability for these subspaces was generated. Then these probability results were input for the second layer classifier. The time series’ label could be predicated by the two-layer classifier. Experiment results showed that this method could both do early classification for time series and achieve higher classification accuracy than only one classifier.

Key words: time series, ensemble learning, classification, sequential subspace, stacked generation