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分位数周期图AI 解读

Two periodogram-like functions, called quantile periodograms, are introduced for spectral analysis of time series. The quantile periodograms are constructed from trigonometric quantile regression and motivated by different interpretations of the ordinary periodogram. Analytical and numerical results demonstrate the capability of the quantile periodograms for detecting hidden periodicity in the quantiles and for providing an additional view of time-series data. A connection…

用于时间序列分析的拉普拉斯周期图AI 解读

A new type of periodogram, called the Laplace periodogram, is derived by replacing least squares with least absolute deviations in the harmonic regression procedure that produces the ordinary periodogram of a time series. An asymptotic analysis reveals a connection between the Laplace periodogram and the zero-crossing spectrum. This relationship provides a theoretical justification for use of the Laplace periodogram as a nonparametric tool for analyzing the serial dependence…

多正弦频率联合估计算法的收敛与偏差校正AI 解读

Twenty years ago, Kay proposed an iterative filtering algorithm (IFA) for jointly estimating the frequencies of multiple complex sinusoids from noisy observations. IFA is based on the fact that the noiseless signal is an autoregressive (AR) process, so the frequency estimation problem can be reformulated as the problem of estimating the AR coefficients. By iterating the cycle of AR coefficient estimation and AR filtering, IFA provides a computationally simple procedure yet…

基于球面小波的空间自适应全球温度场估计AI 解读

Summary The paper considers the problem of estimating the entire temperature field for every location on the globe from scattered surface air temperatures observed by a network of weather-stations. Classical methods such as spherical harmonics and spherical smoothing splines are not efficient in representing data that have inherent multiscale structures. The paper presents an estimation method that can adapt to the multiscale characteristics of the data. The method is based…

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通过参数滤波区分时间序列AI 解读

A new approach to time series discrimination is discussed. The approach, called parametric filtering, combines a parametric filter bank with an analysis of first-order autocorrelation from the filtered time series. As a function of the filter parameter, the first-order autocorrelation is shown to uniquely characterize the correlation structure of the original stationary time series. It is also shown to produce a diagnostic that not only characterizes correlation structures,…

通过参数滤波区分时间序列AI 解读

A new approach to time series discrimination is discussed. The approach, called parametric filtering, combines a parametric filter bank with an analysis of first-order autocorrelation from the filtered time series. As a function of the filter parameter, the first-order autocorrelation is shown to uniquely characterize the correlation structure of the original stationary time series. It is also shown to produce a diagnostic that not only characterizes correlation structures,…

多水平非平稳输入线性系统的盲反卷积AI 解读

A method is proposed to deal with the problem of blind deconvolution of a special non-Gaussian linear process, in which the input to the linear system is a real- or complex-valued multilevel random sequence that satisfies certain regularity conditions. The gist of the method is to apply a linear filter to the observed process and adjust the filter until a multilevel output is obtained. It is shown that the deconvolution problem can be solved (with only scale/rotation and…

单调增益、一阶自相关与过零率AI 解读

The effect of a linear filter with monotone gain on the first-order autocorrelation of a weakly stationary time series is discussed. When the gain is monotone increasing, the first-order autocorrelation cannot increase. Otherwise, when the gain is monotone decreasing, the correlation cannot decrease. Further, when the gain is strictly monotone, the first-order autocorrelation is unchanged if and only if the process is a pure sinusoid with probability 1. Under the Gaussian…