Abstract: One of the main features in financial and economic time series data that trigger attention from researchers is regime shifts or structure breaks. Usually structure breaks occur because of abrupt of change in the government policy, financial and economic crisis and political instability. In recent years wavelet transform becomes more popular in the financial time series analysis and it has better advantages than the other filtering methods such as the traditional technique Fourier transform. In this study, we used Discrete Wavelet Transform (DWT) via Daubechies function and Fast Fourier Transform (FFT) to capture the possibility of regime shifts or structure breaks. The main objective is to detect precisely the changes in the behavior of the Amman stocks market (Jordan) from December 1998 until July 2009. We also discuss the advantages and disadvantages between both methods.`