图 书 馆
学习时间序列ShapeletsLearning Time-Series Shapelets |
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| 课程网址: | http://videolectures.net/kdd2014_grabocka_shapelets/ |
| 主讲教师: | Josif Grabocka |
| 开课单位: | 希尔德斯海姆大学 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  波形图是时间序列的可区分子序列，可以最好地预测目标变量。由于这个原因，最近在时间序列研究领域中，shapelet发现引起了相当大的兴趣。当前，通过评估从系列片段中提取的众多候选对象的预测质量来找到小形。与现有技术相反，本文从学习形状方面提出了一种新颖的观点。提出了一种新的通过分类目标函数对任务进行数学形式化的方法，并采用了一种定制的随机梯度学习算法。所提出的方法使得能够直接学习接近最佳形状的形状，而无需尝试大量的候选对象。此外，我们的方法可以通过捕获真实的前K个形状来学习它们之间的相互作用。大量的实验表明，在28个时间序列数据集中，相对于13个基准而言，获胜和排名的改善具有统计学意义。 |
| 课程简介: | Shapelets are discriminative sub-sequences of time series that best predict the target variable. For this reason, shapelet discovery has recently attracted considerable interest within the time-series research community. Currently shapelets are found by evaluating the prediction qualities of numerous candidates extracted from the series segments. In contrast to the state-of-the-art, this paper proposes a novel perspective in terms of learning shapelets. A new mathematical formalization of the task via a classification objective function is proposed and a tailored stochastic gradient learning algorithm is applied. The proposed method enables learning near-to-optimal shapelets directly without the need to try out lots of candidates. Furthermore, our method can learn true top-K shapelets by capturing their interaction. Extensive experimentation demonstrates statistically significant improvement in terms of wins and ranks against 13 baselines over 28 time-series datasets. |
| 关 键 词: | 预测目标变量; 梯度学习算法; 时间序列数据集; 分类目标函数 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-05-27：zyk |
| 最后编审: | 2021-05-27：zyk |
| 阅读次数: | 182 |