时序数据相似性挖掘算法研究

A NEW ALGORITHM OF SIMILARITY MINING IN TIME SERIES DATA

  • 摘要: 时序数据相似性挖掘是数据挖掘中的重要研究内容.本文针对时序数据进行相似性挖掘方法的研究,通过对时序数据进行离散傅立叶变换(DFT)将其从时域空间变换到频域空间,将时序数据映射为多维空间的点,提出一种基于距离的时序数据相似性挖掘算法,并对某钢铁企业电力负荷时序数据进行仿真实验,实验结果表明了算法的有效性.

     

    Abstract: Similarity retrieval in time series data is an important task in data mining. In this paper, the similarity mining method for time series data is investigated. DFT is used to transform the time series data from time domain to frequency domain. The time series data can be mapped into the multidimensional points in multidimensional space. We proposed a distanced-based algorithm to mine the similar time sequences with given time sequence. The time series data of the power load of a steel plant are used for simulation. The simulation results show the effectiveness of the algorithm.

     

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