基于k平面聚类的时序数据粒化和预测

Time series granulation and forecasting based on k-plane clustering

  • 摘要: 设计了一种针对时间序列的k平面聚类算法,在时间维度和数值维度上同时对所有时序数据点进行聚类,从而将所有数据点划分到若干个不等长的时间窗口.在每个时间窗口上建立线性模糊信息粒子,最终得到原始时间序列的分段线性粒化表示.为便于采用模糊推理系统进行时间序列的预测,还定义了这些线性粒子的距离,从而构建了一种基于线性模糊信息粒子的预测方法.实验表明,所提出的预测方法可以有效地对具有伪周期的时间序列进行长期预测.

     

    Abstract: A special k-plane algorithm to cluster all data in a time series into a number of time windows of unequal-length in both time and numerical domains is designed here.In each time window, a linear fuzzy information granule is established, then piecewise linear granulation representation of original time series is obtained.We introduce a distance measure for two linear information granules of unequal size to construct prediction of granular time series based on fuzzy inference.The proposed forecasting could complete long-term prediction for time series with pseudo-period.

     

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