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Symmetry or asymmetry of the business cycle is an important issue in order to select the behavior patterns and prediction of macroeconomic fluctuations. Factors such as oil prices, the financial crisis, uncertainty, the delay on learning, etc., Can cause lack of symmetry in the cycle. Decomposition of the business cycle by wavelet transform, which is strong instrument for processing data, and reviews of the presence or absence of symmetry at each decomposed level, will allow to obtain more information about different frequencies of business cycle. This helps policy makers to adopt appropriate counter-cyclical policies. Wavelet analysis enabled us to investigate symmetry of high and low frequency components of seasonal GDP during 1989-2011. Using Wavelet Symlet was observed, which at least in the low-frequency component, there is asymmetry. Another advantage of this study is selecting model for prediction of each decomposed level separately. This would reduce forecast error.