• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Software Engineering and Service Science >Diversified Representation Approach for Time Series Using Storm
【24h】

Diversified Representation Approach for Time Series Using Storm

机译：使用风暴的时间序列的多种表示方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With the burgeoning of IoE (Internet of Everything), massive numbers of IoT devices in entensive fields are continuously producing huge number of time series, named as streaming time series (STS). The high dimensionality and dynamic uncertainty of STS lead to the main challenge on traditional time series data mining research. Accordingly, time series representation methods could not only reduce the original high dimensionality of streaming time series, but also contain the main temporal features of raw time series. More importantly, time series representation has been regarded as an necessary preprocessing tool to provide data support for the smooth progress of follow-up research. In this paper, we propose a novel online time series representation approach called continuous segmentation and diversified representation framework (CSDRF) for streaming time series, which contains two different types of time series representation results. The subsequent experiments have been conducted to demonstrate that CSDRF could not only provide the corresponding results to meet the diverse needs of different users, but also provide the corresponding qualified symbolic representation results for time series clustering.
机译：随着IoE（万物互联）的蓬勃发展，大量领域的IoT设备不断产生大量的时间序列，称为流时间序列（STS）。 STS的高维度和动态不确定性导致传统时间序列数据挖掘研究面临的主要挑战。因此，时间序列表示方法不仅可以减少流式时间序列的原始高维性，而且可以包含原始时间序列的主要时间特征。更重要的是，时间序列表示已被视为必要的预处理工具，可为后续研究的顺利进行提供数据支持。在本文中，我们提出了一种新颖的在线时间序列表示方法，称为流分割时间序列的连续分割和多样化表示框架（CSDRF），其中包含两种不同类型的时间序列表示结果。进行的后续实验表明，CSDRF不仅可以提供相应的结果来满足不同用户的不同需求，而且还可以为时间序列聚类提供相应的合格符号表示结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号