A time series is said to have moving seasonality when there are changes in the seasonal pattern. There is evidence that macroeconomic series show moving seasonality. Currently, to perform a seasonal adjustment, IBGE uses the program X-12-ARIMA, which implements the seasonal adjustment method X-11. This method is worldwide adopted by official statistical agencies. However, when a time series shows changing seasonal patterns, the X-11 seasonal adjustment method generates unreliable estimates. This thesis proposes two designs of filters to extract seasonal components in the frequency domain, that are suitable for series with stable seasonality and for those with moving seasonality. The first filter, named WLS-seasonal filter, uses criteria based on least squares. The performance of this filter is assessed based on artificial seasonal series for monthly and quarterly data, based on the characteristics of real macroeconomic series. The results are compared with the ones of X-11 method, and the situations in which this filter is superior to X-11 are identified. Taking into account the fact that the performance of the WLS-seasonal filter improves in relation to the one of X-11 the higher the ratio between the variation of seasonality and irregular intensity, the design of a second filter was developed. This new filter combines the approach of weighted least squares with the Chebyshev filters characteristics, simultaneously minimizing the error in estimating the seasonal component and the influence of the irregular component. It was named WLS-Chebyshev-seasonal filter. The performance of this new filter is compared with the one of the WLS-seasonal filter, and some improvements are observed.