Using dynamic time warping distances as features for improved time series classification
Using dynamic time warping distances as features for improved time series classification
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DOI：
10.1007/s10618-015-0418-x
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发表时间：
2016-03-01
影响因子：
4.8
通讯作者：
Kate, Rohit J.
中科院分区：
文献类型：
--
作者：
Kate, Rohit J.
动态时间规整(DTW)已被证明是时间序列的一种非常强大的距离度量。DTW与最简单的机器学习方法之一--最近邻法相结合，很难令人信服地在时间序列分类任务中表现得更好。在本文中，我们提出了一种简单的时间序列分类技术，它利用了DTW在这一任务上的优势。但该技术不是直接使用DTW作为距离度量来找到最近的邻居，而是使用DTW来创建新的特征，然后将这些特征提供给标准的机器学习方法。我们的实验表明，在47个UCR时间序列基准数据集中的31个上，我们的技术比最近邻DTW方法有更好的性能。此外，该方法可以很容易地扩展为与其他方法结合使用。特别是，我们证明了当与符号聚合近似(SAX)方法相结合时，它在47个UCR数据集中的37个上得到了改进。因此，该方法还提供了一种将基于距离的方法(如DTW)与基于特征的方法(如SAX)相结合的机制。我们还表明，通过集成来组合所提出的分类器进一步提高了时间序列分类的性能。
Dynamic time warping (DTW) has proven itself to be an exceptionally strong distance measure for time series. DTW in combination with one-nearest neighbor, one of the simplest machine learning methods, has been difficult to convincingly outperform on the time series classification task. In this paper, we present a simple technique for time series classification that exploits DTW's strength on this task. But instead of directly using DTW as a distance measure to find nearest neighbors, the technique uses DTW to create new features which are then given to a standard machine learning method. We experimentally show that our technique improves over one-nearest neighbor DTW on 31 out of 47 UCR time series benchmark datasets. In addition, this method can be easily extended to be used in combination with other methods. In particular, we show that when combined with the symbolic aggregate approximation (SAX) method, it improves over it on 37 out of 47 UCR datasets. Thus the proposed method also provides a mechanism to combine distance-based methods like DTW with feature-based methods like SAX. We also show that combining the proposed classifiers through ensembles further improves the performance on time series classification.