提出了“利用反双曲正弦函数变换提高数据列光滑程度”的新结论，获得了递增时间序列改善的自回归预测新方法。
A new conclusion is put forward, in which the smooth degree of the data row can be enhanced by means of the arc-hyperbolic sine function transformation.
本文介绍自回归谱分析法的另一种算法—前后向最小二乘法(LS算法)在亚毫米波付里叶变换谱中的应用。
The least square algorithm using forward and backward linear prediction (LS algorithm) for the autoregressive spectral estimates of the SMMW Fourier transform spectroscopy data is presented.
证明了利用反双曲正弦函数变换能提高数据列的光滑程度，给出了改善的自回归预测方法，并且举例加以论证。
This paper proves that the smooth degree of a data row can be increased by transforming the counter-hyperbolic sine function.
本文介绍了时间序列分析的线性谱(快速傅里叶变换fft谱)和非线性谱(自回归ar谱)。
In this paper time series spectra, both linear spectrum (FFT spectrum) and nonlinear spectrum (ar spectrum) are introduced briefly.
本文介绍了时间序列分析的线性谱(快速傅里叶变换fft谱)和非线性谱(自回归ar谱)。
In this paper time series spectra, both linear spectrum (FFT spectrum) and nonlinear spectrum (ar spectrum) are introduced briefly.
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