We use new and established methodologies in multivariate time series analysis to study the dynamics of 414 Australian hydrological stations' streamflow. First, we analyze our collection of time series in the temporal domain, and compare the similarity in hydrological stations' candidate trajectories. Then, we introduce a Whittle Likelihood-based optimization framework to study the collective similarity in periodic phenomena among our collection of stations. Having identified noteworthy similarity in the temporal and spectral domains, we introduce an algorithmic procedure to estimate a governing hydrological streamflow process across Australia. To determine the stability of such behaviours over time, we then study the evolution of the governing dynamics and underlying time series with time-varying applications of principal components analysis (PCA) and spectral analysis.
翻译:我们利用多变时间序列分析的新方法和既定方法研究414个澳大利亚水文站流流的动态。 首先,我们分析时间范围内的时间序列,比较水文站候选轨迹的相似性。 然后,我们引入一个基于惠特尔 " 相似性 " 的优化框架,以研究我们收集的台站之间周期性现象的集体相似性。我们查明了时间和光谱范围内值得注意的相似性,我们引入了算法程序,以估计整个澳大利亚的水文流流过程。为了确定这种行为在时间范围内的稳定性,我们随后研究主要组成部分分析(PCA)和光谱分析(PCDA)的分阶段应用和时间变化性分析。