Unknown

Dataset Information

0

Application of empirical mode decomposition with local linear quantile regression in financial time series forecasting.


ABSTRACT: 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.

SUBMITTER: Jaber AM 

PROVIDER: S-EPMC4130315 | biostudies-other | 2014

REPOSITORIES: biostudies-other

Similar Datasets

| S-EPMC7924725 | biostudies-literature
| S-EPMC9488768 | biostudies-literature
| S-EPMC9819685 | biostudies-literature
| S-EPMC5863930 | biostudies-literature
| S-EPMC7419003 | biostudies-literature
| S-EPMC4221108 | biostudies-literature
| S-EPMC5319685 | biostudies-literature
| S-EPMC6738919 | biostudies-other
| S-EPMC9322773 | biostudies-literature
| S-EPMC5667718 | biostudies-literature