Dette, HolgerKutta, Tim2019-11-192019-11-192019http://hdl.handle.net/2003/3838610.17877/DE290R-20319Detecting structural changes in functional data is a prominent topic in statistical literature. However not all trends in the data are important in applications, but only those of large enough in uence. In this paper we address the problem of identifying relevant changes in the eigenfunctions and eigenvalues of covariance kernels of L^2[0; 1]- valued time series. By self-normalization techniques we derive pivotal, asymptotically consistent tests for relevant changes in these characteristics of the second order structure and investigate their finite sample properties in a simulation study. The applicability of our approach is demonstrated analyzing German annual temperature data.enDiscussion Paper / SFB823;27/2019functional time seriesself-normalizationeigenvalueseigenfunctionsrelevant changes310330620working paper