21Jun 2017

APPLICATION OF WAVELET REGRESSION WITH LOCAL LINEAR QUANTILE REGRESSION IN FINANCIAL TIME SERIES FORECASTING.

  • Department of Mathematics, Faculty of Science, Damietta University, Damietta, Egypt.
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The classical wavelet methods suffering from boundary problems caused by the application of the wavelet transformations to a finite signal, to treatment boundary problems with wavelet regression, we propose a simple method that decreasing bias at the boundaries, it is based on a combination of wavelet functions and local linear quantile regression (WR- LLQ). We use the proposed technique to forecast stock index time series. Detailed experiments are implemented for the proposed method, in which WR- LLQ, WR, and WR-LP methods are compared. The proposed WR- LLQ model is determined to be superior to the WR and WR-LP methods in predicting the stock closing prices.

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[M. A. Ghazal, W. Alabeid and Gh. Alshreef. (2017); APPLICATION OF WAVELET REGRESSION WITH LOCAL LINEAR QUANTILE REGRESSION IN FINANCIAL TIME SERIES FORECASTING. Int. J. of Adv. Res. 5 (Jun). 1269-1273] (ISSN 2320-5407). www.journalijar.com

wafa Mohamed Alabeid
Department of Mathematics, Faculty of Science, Damietta University, Damietta, Egypt

DOI:

Article DOI: 10.21474/IJAR01/4534      
DOI URL: https://dx.doi.org/10.21474/IJAR01/4534

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