A Wavelet Characterization of Continuous‐Time Periodically Correlated Processes with Application to Simulation
提出一种基于无限维平稳过程线性组合的小波方法，用于刻画连续时间周期相关过程，并给出快速、分布自由的模拟算法，通过四个例子验证了模拟数据具有所需周期。
We introduce a wavelet characterization of continuous‐time periodically correlated processes based on a linear combination of infinite‐dimensional stationary processes. The finite version of this linear combination converges to the main process. The first‐order and second‐order estimators based on the wavelets are presented. Under a simple and easy algorithm, the periodically correlated process is simulated for a given autocovariance function. The proposed algorithm has two main advantages: first, it is fast, and second, it is distribution free. We indicate through four examples that the simulated data are periodically correlated with the desired period.