A review on time series data mining
A review on time series data mining
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DOI：
10.1016/j.engappai.2010.09.007
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发表时间：
2011-02-01
影响因子：
8
通讯作者：
Fu, Tak-chung
中科院分区：
文献类型：
--
作者：
Fu, Tak-chung
时间序列是一类重要的时态数据对象，它可以很容易地从科学和金融应用中获得。时间序列是按时间顺序进行的观测的集合。时间序列数据具有数据量大、维数高、需要不断更新等特点。时间序列数据具有数值性和连续性的特点，通常被看作是一个整体，而不是单个的数值场。时间序列数据的日益广泛应用引发了数据挖掘领域的大量研究和开发尝试。在过去的十年中，大量的时间序列数据挖掘的研究可能会阻碍感兴趣的研究人员进入，由于其复杂性。本文对现有的时间序列数据挖掘研究进行了全面的修正。它们通常分为表示和索引，相似性度量，分割，可视化和挖掘。此外，国家的最先进的研究问题也突出。本文的主要目的是作为一个词汇表，感兴趣的研究人员有一个全面的了解目前的时间序列数据挖掘的发展，并确定其潜在的研究方向，以进一步研究。(C)2010爱思唯尔有限公司版权所有。
Time series is an important class of temporal data objects and it can be easily obtained from scientific and financial applications. A time series is a collection of observations made chronologically. The nature of time series data includes: large in data size, high dimensionality and necessary to update continuously. Moreover time series data, which is characterized by its numerical and continuous nature, is always considered as a whole instead of individual numerical field. The increasing use of time series data has initiated a great deal of research and development attempts in the field of data mining. The abundant research on time series data mining in the last decade could hamper the entry of interested researchers, due to its complexity. In this paper, a comprehensive revision on the existing time series data mining research is given. They are generally categorized into representation and indexing, similarity measure, segmentation, visualization and mining. Moreover state-of-the-art research issues are also highlighted. The primary objective of this paper is to serve as a glossary for interested researchers to have an overall picture on the current time series data mining development and identify their potential research direction to further investigation. (C) 2010 Elsevier Ltd. All rights reserved.