摘要翻译：
具有动态时间规整(DTW)距离的1-最近邻分类器是时间序列领域最有效的分类器之一。自从全局约束引入语音界以来，人们提出了许多全局约束模型，包括Sakoe-Chiba（S-C）频带、Itakura平行四边形和Ratanamahatana-Keogh（R-K）频带。R-K带是一种通用的全局约束模型，可以有效地表示任意形状和大小的全局约束。然而，我们需要一个好的学习算法来发现最合适的R-K波段集合，而目前的R-K波段学习算法仍然存在“过拟合”现象。本文提出了两种新的学习算法，即带边界提取算法和迭代学习算法。从每类中所有可能的翘曲路径的界限计算频带边界提取，并在原有R-K频带学习的基础上调整迭代学习。我们还使用了一种著名的聚类验证技术&剪影指数作为启发式函数，并使用下限函数LB_Keogh来提高预测速度。在2007年SIGKDD同时举办的时间序列分类研讨会和挑战赛中的20个数据集被用来评估我们的方法。
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英文标题：
《Learning DTW Global Constraint for Time Series Classification》
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作者：
Vit Niennattrakul and Chotirat Ann Ratanamahatana
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  1-Nearest Neighbor with the Dynamic Time Warping (DTW) distance is one of the most effective classifiers on time series domain. Since the global constraint has been introduced in speech community, many global constraint models have been proposed including Sakoe-Chiba (S-C) band, Itakura Parallelogram, and Ratanamahatana-Keogh (R-K) band. The R-K band is a general global constraint model that can represent any global constraints with arbitrary shape and size effectively. However, we need a good learning algorithm to discover the most suitable set of R-K bands, and the current R-K band learning algorithm still suffers from an 'overfitting' phenomenon. In this paper, we propose two new learning algorithms, i.e., band boundary extraction algorithm and iterative learning algorithm. The band boundary extraction is calculated from the bound of all possible warping paths in each class, and the iterative learning is adjusted from the original R-K band learning. We also use a Silhouette index, a well-known clustering validation technique, as a heuristic function, and the lower bound function, LB_Keogh, to enhance the prediction speed. Twenty datasets, from the Workshop and Challenge on Time Series Classification, held in conjunction of the SIGKDD 2007, are used to evaluate our approach.
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PDF链接：
https://arxiv.org/pdf/0903.0041