نوفه زدایی

Each set of wavelet coefficients contains part of the time series at different time scales. Implementing wavelet transforms by utilizing the best wavelets at the right levels will have a significant impact on financial analysis results. Therefore, the present study seeks to present a proposed model for measuring firm financial sustainability by using an economophysical method using a sample of 86 companies listed in Tehran Stock Exchange during 2015-2019. The results of the research hypothesis show that the prediction of financial sustainability based on the economophysical method can provide better results. According to the findings of the study, it can be stated that the dendrogram observed in the Dabshiz wavelet is more accurate in decoupling and consequently clustering.

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.

Stock index as time series are non-stationary and highly noisy due to the fact that stock markets are affected by a variety of factors. It is regarded as one of the most challenging application of time series forecasting. Predicting stock index with the noisy data directly is usually subject to large errors. In this paper we compare forecasting the stock index via Wavelet De-noising-based Neural Network (WDNN) with forecasting stock index via single neural network. The daily Tehran Stock index from April 2006 to June 2013 are used to compare the application of the WDNN in predicting the stock index. Experimental results show that de-noising with wavelet transform outperforms the single neural network.