时间序列数据降维及特征表示新方法

数据降维和特征表示是解决时间序列维灾问题的关键技术和重要方法, 它们在时间序列数据挖掘中起基础性作用. 鉴于此, 提出一种新的时间序列数据降维和特征表示方法, 利用正交多项式回归模型对时间序列实现特征提取, 结合特征序列长度对时间序列的拟合分析结果, 运用奇异值分解方法对特征序列进一步降维处理, 进而得到保存大部分信息且维数更低的特征序列. 数值实验结果表明, 新方法可以在维度较低的特征空间下取得较好的数据挖掘聚类和分类效果.

Novel method of dimensionality reduction and feature representation for time series

Dimensionality reduction and feature representation are the key technique and important methods to address the issue of dimensionality curse for time series. Meanwhile, they are a basis task in the field of time series data mining. Therefore, a novel method of dimensionality reduction and feature representation is proposed. An orthogonal polynomial regression model is used to obtain a feature sequence from an original time series. Furthermore, singular value decomposition combining with the fitting results of the feature sequence to time series is used to reduce the dimensionality of feature sequence and obtain another feature sequence with lower dimension to retain most of the information. The results of numerical experiments demonstrate that the novel method can obtain a good effect of clustering and classification in time series data mining under the space with lower dimensionality.