Recent developments of time series analysis in environmental impact studies

Abstract

Time series analysis, particularly intervention analysis, is commonly employed in impact studies of environmental data. Environmental time series are susceptible to exogenous variations and often contain various types of outliers. Outliers, depending upon the time of their occurrences and nature, can have substantial impact on the estimates of intervention effects and their test statistics. Hence, outlier detection and adjustment should be an indispensable part of an intervention analysis. In this paper, an iterative procedure for the joint estimation of model parameters and outlier effects is employed with the intervention analysis. We find that this joint estimation procedure not only produces more reliable estimates of intervention effects, but also provides information on outliers, which is valuable in many respects. As a special case of outlier adjustment, this joint estimation procedure can also be used to estimate the values of missing data in a time series. Two data sets are used to illustrate the application of intervention analysis with outlier adjustment. We find the results under the new approach more meaningful and informative than those of traditional analyses. A third data set is used to illustrate time series modeling with missing data and outliers. Outlier analysis is not only useful in providing information for retroactive investigation of unusual observations in a time series, but more importantly, it can be used in conjunction with data collection and thus improve the quality of the data.