Classification of non-stationary time series
Classification of non-stationary time series
复制标题
非平稳时间序列的分类
作者：
Krzemieniewska K
在本文中，我们考虑非平稳时间序列的分类问题。我们介绍的方法是基于局部平稳小波范式，并试图考虑到这样一个事实，即在被分析的信号中可能存在类内变化。具体来说，我们试图确定每个训练组中最稳定的频谱系数，并使用这些来分类一个新的，以前看不见的时间序列。在模拟示例和由工业合作者提供的气溶胶喷雾示例中，发现我们的方法在与现有技术相比时产生上级分类性能。
In this paper we consider the problem of classifying non‐stationary time series. The method that we introduce is based on the locally stationary wavelet paradigm and seeks to take account of the fact that there may be within‐class variation in the signals being analysed. Specifically, we seek to identify the most stable spectral coefficients within each training group and use these to classify a new, previously unseen, time series. In both simulated examples and an aerosol spray example provided by an industrial collaborator, our approach is found to yield superior classification performance when compared against the current state of the art. Copyright © 2014 John Wiley & Sons, Ltd.