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...
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | أطروحة |
| اللغة: | الإنجليزية |
| منشور في: |
2018
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://eprints.usm.my/43955/ |
| Abstract | Abstract here |
| _version_ | 1855629782862004224 |
|---|---|
| author | Awajan, Ahmad Mohammad Al-Abd |
| author_facet | Awajan, Ahmad Mohammad Al-Abd |
| author_sort | Awajan, Ahmad Mohammad Al-Abd |
| description | 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. |
| first_indexed | 2025-10-17T08:23:41Z |
| format | Thesis |
| id | usm-43955 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2025-10-17T08:23:41Z |
| publishDate | 2018 |
| record_format | EPrints |
| record_pdf | Restricted |
| spelling | usm-439552019-04-12T05:24:50Z http://eprints.usm.my/43955/ Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition Awajan, Ahmad Mohammad Al-Abd QA1-939 Mathematics 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. 2018-05 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/43955/1/AHMAD%20MOHAMMAD%20AL-%20ABD%20AWAJAN.pdf Awajan, Ahmad Mohammad Al-Abd (2018) Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA1-939 Mathematics Awajan, Ahmad Mohammad Al-Abd Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition |
| thesis_level | PhD |
| title | Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition |
| title_full | Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition |
| title_fullStr | Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition |
| title_full_unstemmed | Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition |
| title_short | Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition |
| title_sort | forecasting performance of nonlinear and nonstationary stock market data using empirical mode decomposition |
| topic | QA1-939 Mathematics |
| url | http://eprints.usm.my/43955/ |
| work_keys_str_mv | AT awajanahmadmohammadalabd forecastingperformanceofnonlinearandnonstationarystockmarketdatausingempiricalmodedecomposition |