基于神经网络的时间序列相似模式发现方法

Method for Similar Pattern Discovery in Time Series Based on Neural Network

基于无监督学习神经网络聚类原理,提出一种时间序列相似模式发现方法.通过快速离散余弦变换将序列映射到相应的特征模式空间,不但实现维数简约,而且克服传统神经网络不能处理过程序列的局限性.分析人工神经网络作为相似性度量模型的优越性,用"黑箱式"的网络权值代替传统的距离度量方法,并在此基础上实现相似模式的全部配对发现算法.对实际飞行数据仿真结果表明该方法的正确性,同时具有多尺度特性,可有效反映不同分辨率下序列间的相似程度.

According to the unsupervised neural network theory of clustering, a method is proposed for similar pattern discovery in time series database. Aiming at the poor capability of the neural network for handling the time change process sequence, the original data are mapped into the feature pattern space by means of fast discrete cosine transform （FDCT） for dimension reduction. The advantages of artificial neural network as similarity measurement model are analyzed and the range query algorithm is presented. The simulation results show that the proposed algorithm has the property of multi-scale, and compared with Euclidean distance and Slop distance, it can reflect similarities of time series under various resolutions.