A Comparison between the Daubechies wavelet transformation and the Fast Fourier Transform in Analyzing Insurance Time Series Data

Abstract: During the current financial crisis, the insurance enterprise received more attention. As a result, this paper displays two types of filtering time series representing the Wavelet analysis and the Fourier analysis. We used the insurance income time series data taken from the Amman Stocks Market (Jordan) for a certain period of time. First, according to the growing up in the Wavelet transformation in recent years and it has a better advantage than the traditional Fourier transformation method. Therefore, we compared the Fast Fourier Transformation (FFT) and discrete wavelet transformation using Debuchies algorithm (dbN) by analyzing the original insurance time series data. New idea has decided the most convenient model was utilized by the autocorrelation function for fast Fourier transformation and Debuchies wavelet transformation. Some statistical and numerical results have obtained by using MATLAB (Wavelet tool).