图 书 馆
索引和挖掘时间序列Indexing and Mining Time Sequences |
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| 课程网址: | http://videolectures.net/kdd2010_faloutsos_li_imt/ |
| 主讲教师: | Christos Faloutsos, Lei Li |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | How can we find patterns in a sequence of sensor measurements (eg., a sequence of temperatures, or water-pollutant measurements)? How can we compress it? What are the major tools for forecasting and outlier detection? The objective of this tutorial is to provide a concise and intuitive overview of the most important tools that can help us find patterns in sensor sequences. Sensor data analysis becomes of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-sensor processing abilities. We review the state of the art in three related fields: (a) fast similarity search for time sequences, (b) linear forecasting with the traditional AR (autoregressive) and ARIMA methodologies, (c) non-linear forecasting, for chaotic/self-similar time sequences, using lag-plots and fractals, and (d) Kalman filters. The emphasis of the tutorial is to give the intuition behind these powerful tools, which is usually lost in the technical literature, as well as to give case studies that illustrate their practical use. |
| 关 键 词: | 传感器; 异常值检测; 数据分析 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-11：lxf |
| 阅读次数: | 47 |