时间序列可变尺度的时频特征求解及其分类

对于许多实际应用来说，获取多个不同窗口尺度上的模式，有助于发现时间序列的不同规律性特征.同时，通过对时间序列时域和频域两方面的分析，有助于挖掘更多的知识.提出了一种新的基于可变尺度的时域频域辨别性特征挖掘方法以及应用于分类的算法.主要采用了不同尺度窗口、符号聚合近似技术以及符号傅里叶近似技术等，以有效地发掘时间序列不同尺度时域频域模式；与此同时，使用统计学方法挖掘部分最具辨别性的特征用于时间序列分类，有效地降低了算法时间复杂度.在多个数据集上的对比实验结果，说明了该算法具有较高的准确率；在真实数据集上的解析，表明了该算法具有更强的可解释性.同时，该算法可扩展应用到多维时间序列分类问题中.

Time Series Pattern Discovery and Classification with Variable Scales in Time-frequency Domains

For many real-world applications, capturing patterns at diverse window scales can help to discover the different periodicity of time series. At the same time, it is helpful to gain more knowledge by analyzing time series from both time-domain and frequency-domain. This study proposes a novel method to detect distinctive patterns at variable scales in time-domain and frequency-domain of time series, and discuss its application on classification. This method integrates multiple scales, the symbolic approximation and symbolic Fourier approximation techniques to explore multi-scales and multi-domain patterns efficiently in time series. Meanwhile, statistical method is applied to select some of the most discriminative patterns for time series classification, which also can effectively reduce time complexity of the algorithm. The experiments performed on various datasets demonstrate that the proposed method has higher accuracy and better interpretability. In addition, it can be extended to multi-dimensional time series easily.