We provide a theoretical justification for the application of
higher degree fuzzy transform in time series analysis. We show that
the higher degree fuzzy transform technique suppresses high
frequencies in time series, which is one of the important
assumptions in a successful extraction of the trend-cycle of time
series. More precisely, assuming that a time series can be
additively decomposed into a trend (trend-cycle), a seasonal
component and a noise, we demonstrate that high frequencies, which
appear in the seasonal component, can be arbitrarily suppressed
using the fuzzy transform of higher degree provided that a
reasonable setting of a generalized uniform fuzzy partition is
considered.