Wavelet regression Combined with Local Linear Quantile Regression for Automatic Boundary Correction

Abstract

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.