为提高短期负荷预测的精度,根据短期负荷的基本特性,提出了一种将相空间重构理论(phase space reconstruction space,PSRT)与Elman神经网络相结合的短期负荷多步预测模型.
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Applying phase space reconstruction method to divide time series into segments, we have mapped original series into multidimensional data space.
应用相空间重构技术对时间序列进行分割，将原序列映射到多维的数据空间中。
Analyzing the change laws of distance in phase space to deformation in backfill, a prediction model is built based on phase space reconstruction with gray theory.
用灰色理论研究充填体变形在相空间中相点距离的演变规律，建立了重构相空间的灰色预测模型。
By phase space reconstruction, choosing the most suitable delay time and embedding dimension in order to embed time series which reflect the demanding into the phase space.
通过相空间重构技术，选取合适的延迟时间和嵌入维数，将反映市场需求的时间序列嵌入到相空间中。
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