基于自相关系数和LVQ神经网络的时间序列自动平稳检测

M. Poulos, S. Papavlasopoulos
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引用次数: 7

摘要

使用自相关系数(ACC)和LVQ神经网络的时间序列数据挖掘在本工作中得到了解决——据我们所知,这个问题尚未在信号处理框架中看到。对真实数据的实时时间序列数据进行神经网络分类,试图通过实验研究时间序列数据与平稳时间序列属性隐含信息之间的联系。最后,将通过一个拟合良好的LVQ神经网络来测试ACC的能力,该网络在预测时间序列方面取得了令人满意的结果。
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Automatic stationary detection of time series using auto-correlation coefficients and LVQ — Neural network
A data mining of Time Series using Autocorrelation Coefficients (ACC) and LVQ -Neural Network is addressed in this work-a problem that has not yet been seen in a signal processing framework, to the best of our knowledge. Neural network classification was performed on real Time series Data of real data, in an attempt to experimentally investigate the connection between Time Series data and hidden information about the properties of stationary Time Series. Finally, the ability of the ACC will be tested via a well fitted LVQ neural network which gives satisfactory results in predicting Time Series.
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