A Spectral Domain Test for Stationarity of Spatio‐Temporal Data
针对离散等距时间、不规则空间位置的时空数据，基于傅里叶变换的平稳性检验方法，帮助判断数据是否满足平稳性假设，避免预测偏差。
Many random phenomena in the environmental and geophysical sciences are functions of both space and time; these are usually called spatio‐temporal processes. Typically, the spatio‐temporal process is observed over discrete equidistant time and at irregularly spaced locations in space. One important aim is to develop statistical models based on what is observed. While doing so a commonly used assumption is that the underlying spatio‐temporal process is stationary. If this assumption does not hold, then either the mean or the covariance function is misspecified. This can, for example, lead to inaccurate predictions. In this article we propose a test for spatio‐temporal stationarity. The test is based on the dichotomy that Fourier transforms of stochastic processes are near uncorrelated if the process is second‐order stationary but correlated if the process is second‐order nonstationary. Using this as motivation, a discrete Fourier transform for spatio‐temporal data over discrete equidistant times but on irregularly spaced spatial locations is defined. Two statistics which measure the degree of correlation in the discrete Fourier transforms are proposed. These statistics are used to test for spatio‐temporal stationarity. It is shown that the same statistics can also be adapted to test for the one‐way stationarity (either spatial or temporal stationarity). The proposed methodology is illustrated with a small simulation study.