论文标题
贝叶斯非参数共享多序列时间序列细分
Bayesian nonparametric shared multi-sequence time series segmentation
论文作者
论文摘要
在本文中，我们介绍了一种使用贝叶斯非参数的工具进行分割时间序列数据的方法。我们考虑将一组时间序列数据的时间分割为代表性的固定段的任务。我们使用高斯工艺（GP）先验来强加我们对基本固定段特征的知识，并使用非参数分布将序列分配到此类段中，该序列是根据段长度的先前分布提出的。鉴于分割，可以将模型视为高斯混合模型的变体，其中使用GP的协方差函数描述了混合物组件。我们证明了模型对合成数据以及心跳的实时序列数据的有效性，在这些数据中，任务是将节拍的指示类型分割，并将心跳记录分类为与健康和异常心脏声音相对应的类。
In this paper, we introduce a method for segmenting time series data using tools from Bayesian nonparametrics. We consider the task of temporal segmentation of a set of time series data into representative stationary segments. We use Gaussian process (GP) priors to impose our knowledge about the characteristics of the underlying stationary segments, and use a nonparametric distribution to partition the sequences into such segments, formulated in terms of a prior distribution on segment length. Given the segmentation, the model can be viewed as a variant of a Gaussian mixture model where the mixture components are described using the covariance function of a GP. We demonstrate the effectiveness of our model on synthetic data as well as on real time-series data of heartbeats where the task is to segment the indicative types of beats and to classify the heartbeat recordings into classes that correspond to healthy and abnormal heart sounds.
