A real-time ensemble classification algorithm for time series data
Xianglei Zhu, Shuai Zhao, Yaodong Yang, Hongyao Tang, Zan Wang, Jianye Hao · 2017
Due to the ubiquity of time series data, time series classification has important applications in a wide range of practical scenarios. In this paper, we propose a novel ensemble approach which combines two state-of-the-art classifisers: KMeans-DBA-KNN (K-D-K) and Long Short-Term Memory (LSTM). We demonstrate the effectiveness of our approach by comparing with state-of-the-art time series classification algorithms on benchmark time-series datasets, and show that our ensemble algorithm outperforms the existing ones.