论文标题
时间序列数据的动态聚类
Dynamic clustering of time series data
论文作者
论文摘要
我们提出了一种基于动态线性模型的多元时间序列数据的新方法。尽管通常的时间序列聚类方法获得静态成员参数，但我们的建议允许每个时间序列随着时间的推移动态更改群集成员资格。在这种情况下，假定为时间序列的混合模型，并且混合物权重的灵活的dirichlet演变可以随着时间的推移而平稳的会员变化。可以通过Gibbs采样获得后验估计和预测，但是根据随机期望最大化和梯度下降，提出了一种更有效的获得点估计的方法。最后，两个应用程序说明了我们提出的模型对单变量和多变量时间序列进行建模的有用性：欧盟国家可再生能源消耗的世界银行指标，以及著名的Gapminder数据集，其中包含各个国家 /地区的人均寿命和GDP。
We propose a new method for clustering multivariate time-series data based on Dynamic Linear Models. Whereas usual time-series clustering methods obtain static membership parameters, our proposal allows each time-series to dynamically change their cluster memberships over time. In this context, a mixture model is assumed for the time series and a flexible Dirichlet evolution for mixture weights allows for smooth membership changes over time. Posterior estimates and predictions can be obtained through Gibbs sampling, but a more efficient method for obtaining point estimates is presented, based on Stochastic Expectation-Maximization and Gradient Descent. Finally, two applications illustrate the usefulness of our proposed model to model both univariate and multivariate time-series: World Bank indicators for the renewable energy consumption of EU nations and the famous Gapminder dataset containing life-expectancy and GDP per capita for various countries.
