Denoising of financial time series using wavelet analysis

every series of Wavelet coefficients includes part of time series in the scale of different time series. Implementation of the wavelet transform, using the best Wavelet at the right levels has significant impact on the results of the results of the financial analysis.
the purpose of this study is to explanation of the importance of the concept of scale-time and the use of different time intervals in checking the behavior of the financial markets to be determined whether the removing noise from the time series can accurate the decisions we have to make in the future or not?
Therefore we analyzed 16 selected index of the Tehran Stock Exchange using software "R" and using Wavelet transformation up to five levels for 250 data then put them all under noise removing process.
In the next step we used two methods for evaluation the noise removing process. one clustering all the selected index in the dendrogram method And the other one time series predictions of total index which includes 500 data and the use of the data that has been noise removed into two methods of Haar wavelet and Daubechies.
The results of both method claim better performance using Wavelet removing noise using Daubechies wavelet in this series. our main goal is using the wavelet analysis and noise removing from time series and using that in financial topics.