图 书 馆
时间序列小单元：数据挖掘的新开始Time Series Shapelets: A New Primitive for Data Mining |
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| 课程网址: | http://videolectures.net/kdd09_ye_tssnpdm/ |
| 主讲教师: | Lexiang Ye |
| 开课单位: | 加利福尼亚大学 |
| 开课时间: | 2009-09-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Classification of time series has been attracting great interest over the past decade. Recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems. While this may be considered good news, given the simplicity of implementing the nearest neighbor algorithm, there are some negative consequences of this. First, the nearest neighbor algorithm requires storing and searching the entire dataset, resulting in a time and space complexity that limits its applicability, especially on resource-limited sensors. Second, beyond mere classification accuracy, we often wish to gain some insight into the data. In this work we introduce a new time series primitive, time series shapelets, which addresses these limitations. Informally, shapelets are time series subsequences which are in some sense maximally representative of a class. As we shall show with extensive empirical evaluations in diverse domains, algorithms based on the time series shapelet primitives can be interpretable, more accurate and significantly faster than state-of-the-art classifiers. |
| 关 键 词: | 时间序列分类; 最近邻算法; 图元算法; 分类精度 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-06：王勇彬(课程编辑志愿者) |
| 阅读次数: | 130 |