Similarity Measure of Time Series Based on Feature Extraction
2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)(2020)
摘要
Time series data mining attracts a lot of attentions in many applications. Similarity measure of time series is a common problem in data mining tasks. Among the existing similarity measures, dynamic time warping can get high accuracy, but the computational cost is expensive. In this paper, we first segment sequences and extract their features; then, a similarity measure is proposed to balance the contradiction between computational cost and accuracy. Finally, experiments are implemented on time series data sets. The experimental results show that our proposed method can effectively improve the computational efficiency of similarity measure.
更多查看译文
关键词
series,similarity measure,feature extraction,dynamic time warping,computational complexity
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要