Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting

Abstract EN

This paper mainly forecasts the daily closing price of stock markets.

We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ).

We use the proposed technique, EMD-LLQ, to forecast two stock index time series.

Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared.

The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.