On Nonparametric Tests for Trend Detection in Seasonal Time Series
Oliver Morell, Roland Fried · 2008
We investigate nonparametric tests for identifying monotone trends in time series as they need weaker assumptions than parametric tests and are more flexible concerning the structure of the trend function. As seasonal effects can falsify the test results, modifications have been suggested which can handle also seasonal data. Diersen and Trenkler [5] propose a test procedure based on records and Hirsch et. al [8] develop a test based on Kendall's test for correlation. The same ideas can be applied to other nonparametric procedures for trend detection. All these procedures assume the observations to be independent. This assumption is often not fulfilled in time series analysis. We use the mentioned test procedures to analyse the time series of the temperature and the rainfall observed in Potsdam (Germany) from 1893 to 2008. As opposed to the rainfall time series, the temperature data show positive autocorrelation. Thus it is also of interest, how the several test procedures behave in case of autocorrelated data.