Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting
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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.
2012 ◽
Vol 2012
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pp. 1-21
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2012 ◽
Vol 11
(02)
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pp. 1250018
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2012 ◽
Vol 16
(5)
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Keyword(s):
2017 ◽
Vol 5
(6)
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pp. 1269-1273
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2018 ◽
Vol 11
(1)
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pp. 1
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