IEEE Trans Pattern Anal Mach Intell. 2013 Mar;35(3):740-54. doi: 10.1109/TPAMI.2012.121. Epub 2012 May 29.
We present a novel framework for analyzing univariate time series data. At the heart of the approach is a versatile algorithm for measuring the similarity of two segments of time series called geometric template matching (GeTeM). First, we use GeTeM to compute a similarity measure for clustering and nearest-neighbor classification. Next, we present a semi-supervised learning algorithm that uses the similarity measure with hierarchical clustering in order to improve classification performance when unlabeled training data are available. Finally, we present a boosting framework called TDEBOOST, which uses an ensemble of GeTeM classifiers. TDEBOOST augments the traditional boosting approach with an additional step in which the features used as inputs to the classifier are adapted at each step to improve the training error. We empirically evaluate the proposed approaches on several datasets, such as accelerometer data collected from wearable sensors and ECG data.
我们提出了一种分析单变量时间序列数据的新框架。该方法的核心是一种用于测量时间序列两段之间相似性的通用算法，称为几何模板匹配（GeTeM）。首先，我们使用 GeTeM 计算相似性度量，用于聚类和最近邻分类。接下来，我们提出了一种半监督学习算法，该算法使用相似性度量和层次聚类，以便在有可用未标记训练数据时提高分类性能。最后，我们提出了一种称为 TDEBOOST 的提升框架，它使用了一个 GeTeM 分类器的集成。TDEBOOST 通过在每个步骤中自适应地调整作为分类器输入的特征，来增强传统的提升方法，以提高训练误差。我们在几个数据集上对所提出的方法进行了实证评估，例如从可穿戴传感器收集的加速度计数据和 ECG 数据。
