本文讨论的主要问题是时间序列分析在预测领域的应用及编程实现。
This article discusses the main problem of time series analysis in the field of forecast application and programming.
基于变分贝叶斯及相空间重构理论，提出了含噪混沌时间序列相空间域线性回归预测模型。
We present a linearly regressive prediction model for noisy chaotic time series phase space based on variational Bayesian and phase space reconstructive theory.
采用线性插值及双线性插值得到预测点位置上的本征模态值。 结构由原风压场协方差分析得到的主坐标和上述新本征模态值获得未布置测压点位置的风压时间序列。
The linear interpolation and bilinear interpolation were employed to obtain the values of the proper modes on locations where the wind pressure time series are to be predicted.
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