The phase space is reconstructed after determining the delay timeτand embedding dimension m of the raw data. And then realize the short-term prediction of pressure data based on the local method.
研究中以采集的原始数据为实验样本,首先确定嵌入延迟τ和嵌入维数m,并对其进行相空间重构,然后利用局域预测法对数据进行单步预测。
参考来源 - 矿用液压支架数据采集与预警系统的研究Based on local linear prediction model of chaotic time series, short-term load forecasting method on multi-embedding dimension is presented.
基于混沌时间序列的局域线性预测模型,提出了多嵌入维的短期负荷预测方法。
参考来源 - 基于电力负荷时间序列混沌特性的短期负荷预测方法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
利用伪最邻近点法确定相空间重构的最小嵌入维数。
The minimum embedding dimension of reconstruction is confirmed by the false nearest neighbours method.
通过用虚假临界点法计算嵌入维数可以使小数据量法更加完善。
The small-data method is improved by false nearest neighbor method calculating embedding dimension.
这一结果基本不受个体、数据长度、嵌入维数以及延迟时间的影响。
This result not change by and large when object, the length of data, embedded dimensions and delay time change.
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