Department of Systems Design Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada.
Environ Monit Assess. 1989 Nov;13(2-3):185-201. doi: 10.1007/BF00394229.
Intervention analysis techniques are described for identifying and statistically modelling trends which may be present in water quality time series. At the exploratory data analysis stage, simple graphical and modelling methods can be employed for visually detecting and examining trends in a time series caused by one or more external interventions. For instance, a plot of a robust locally weighted regression smooth through a graph of the observations over time may reveal trends and other interesting statistical properties contained in the time series. In addition, statistical tests, such as different versions of the nonparametric Mann-Kendall test, can be used to detect the presence of trends caused by unknown or known external interventions. To characterize rigorously and estimate trends which may be known in advance or else detected using exploratory data analysis studies, different parametric methods can be utilized at the confirmatory data analysis stage. Specifically, the time series modelling approach to intervention analysis can be employed to estimate the magnitudes of the changes in the mean level of the series due to the interventions. Particular types of regression models can also be used for estimating trends, especially when there are many missing observations. To demonstrate how intervention analysis methods can be effectively used in environmental impact assessment, representative applications to water quality time series are presented.
干预分析技术用于识别和统计建模水质时间序列中可能存在的趋势。在探索性数据分析阶段，可以使用简单的图形和建模方法，直观地检测和检查由一个或多个外部干预引起的时间序列中的趋势。例如，通过绘制随时间变化的观测值的稳健局部加权回归平滑图，可以揭示时间序列中包含的趋势和其他有趣的统计特性。此外，统计检验，如非参数 Mann-Kendall 检验的不同版本，可用于检测未知或已知外部干预引起的趋势的存在。为了严格描述并估计可能预先知道或通过探索性数据分析研究检测到的趋势，可以在确认性数据分析阶段使用不同的参数方法。具体来说，可以采用时间序列建模方法进行干预分析，以估计由于干预而导致的序列平均水平变化的幅度。还可以使用特定类型的回归模型来估计趋势，特别是当存在许多缺失观测值时。为了说明干预分析方法如何在环境影响评估中有效使用，展示了水质时间序列的代表性应用。
