WIENER–KOLMOGOROV FILTERING, FREQUENCY-SELECTIVE FILTERING, AND POLYNOMIAL REGRESSION
改进了经典维纳-柯尔莫哥洛夫滤波器，使其适用于短非平稳序列，并比较了时域与频域方法，频域方法能无泄漏地分离数据成分，还创新性地解决了趋势数据滤波的启动问题。
Adaptations of the classical Wiener–Kolmogorov filters are described that enable them to be applied to short nonstationary sequences. Alternative filtering methods that operate in the time domain and the frequency domain are described. The frequency-domain methods have the advantage of allowing components of the data to be separated along sharp dividing lines in the frequency domain, without incurring any leakage. The paper contains a novel treatment of the start-up problem that affects the filtering of trended data sequences.