基于时序对齐的k近邻分类器是时间序列分类的基准算法.在实际应用中，同类复杂时间序列经常展现出不同的全局特性.由于传统时序对齐方法平等对待实例特征并忽略其局部辨别特性，因此难以准确、高效地处理此类具有挑战性的时间序列.为了有效对齐并分类复杂时间序列，提出了一种具有辨别性的局部加权动态时间扭曲方法，用于发现同类复杂时间序列的共同点以及异类序列间的不同点.同时，通过迭代学习时间序列对齐点的正例集与负例集，获取每条复杂时间序列中每个特征的辨别性权重.在多个人工和真实数据集上的实验结果表明了基于局部加权对齐策略的k近邻分类器所具有的可解释性与有效性，并将所提出方法扩展至多变量时间序列分类问题中.

Temporal alignment based k-nearest neighbor classifier is a benchmark for time series classification. Since complex time series generally exhibit different global behaviors within classes in real applications, it is difficult for standard alignment, where features are treated equally while local discriminative behaviors are ignored, to handle these challenging time series correctly and efficiently. To facilitate aligning and classifying such complex time series, this paper proposes a discriminative locally weighted dynamic time warping dissimilarity measure that reveals the commonly shared subsequence within classes as well as the most differential subsequence between classes. Meanwhile, time series alignments of positive and negative subsets are employed to learning discriminative weight for each feature of each time series iteratively. Experiments performed on synthetic and real datasets demonstrate that this locally weighted, temporal alignment based k-nearest neighbor classifier is effective in differentiating time series with good interpretability. Extension of the proposed weighting strategy to multivariate time series is also discussed.

国家自然科学基金（61672086，61702030）；中央高校基本科研业务费专项资金（2016RC048，2017YJS036）