Fuzzy transform, time series decomposition

we provide a method for applying the fuzzy transform of higher degree to time series decomposition. We assume that a time series can be decomposed into a trend-cycle, a seasonal component, and an irregular fluctuation. Wedevote theoretical justifications for decomposing it into an additive model. Several examples are considered to demonstrate our methodology.

This paper introduces a novel construction of test spaces in the Ritz-Galerkin method. These spaces are established on the basis of linear spaces of functions that are linear combination of polynomials and fuzzy sets of a generalized uniform fuzzy partition.