Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting
This paper mainly forecasts the daily closing price of stockmarkets.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, EMDLLQ, to forecast two stock index time series. Detail...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Hindawi Publishing Corporation
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| Subjects: | |
| Online Access: | http://eprints.usm.my/38348/1/Application_of_Empirical_Mode_Decomposition_with_Local_Linear_Quantile_Regression_in.pdf http://eprints.usm.my/38348/ http://dx.doi.org/10.1155/2014/708918 |
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| Summary: | This paper mainly forecasts the daily closing price of stockmarkets.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, EMDLLQ,
to forecast two stock index time series. Detailed experiments are implemented for the proposedmethod, in which EMD-LPQ,
EMD, andHolt-Winter methods are compared.The proposed EMD-LPQ model is determined to be superior to the EMDandHolt-
Winter methods in predicting the stock closing prices. |
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