Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition
The stock market indices are typically non-linear and non-stationary with high heteroscedasticity data, which affect the accuracy and validity of the results of traditional forecasting methods. Therefore, this study focuses on decomposition method to solve the problem of non-linearity and non-stati...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2018
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://eprints.usm.my/43955/1/AHMAD%20MOHAMMAD%20AL-%20ABD%20AWAJAN.pdf |
| الوسوم: |
إضافة وسم
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| الملخص: | The stock market indices are typically non-linear and non-stationary with high heteroscedasticity data, which affect the accuracy and validity of the results of traditional
forecasting methods. Therefore, this study focuses on decomposition method to solve the problem of non-linearity and non-stationarity in data with high heteroscedasticity
behavior to improve the accuracy of stock market forecasting. Recently, Empirical mode decomposition (EMD) method has been introduced as an effective technique
for overcoming the non-linearity and non-stationarity in time series data. EMD presents several characteristics that other decomposition methods do not have. |
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