Empirical mode decomposition based on theta method for forecasting daily stock price. Journal of Information and Communication Technology (JICT), 19 (4). pp. 533-558. ISSN 2180-3862 (2020)
Abstract
Forecasting is a challenging task as time series data exhibit many features that cannot be captured by a single model. Therefore, many researchers have proposed various hybrid models in order to accommodate these features to improve forecasting results. This work proposed a hybrid method between Empirical Mode Decomposition (EMD) and Theta methods by considering better forecasting potentiality. Both EMD and Theta are efficient methods in their own ground of tasks for decomposition and forecasting, respectively. Combining them to obtain a better synergic outcome deserves consideration. EMD decomposed the training data from each of the five Financial Times Stock Exchange 100 Index (FTSE 100 Index) companies’ stock price time series data into Intrinsic Mode Functions (IMF) and residue.
| Item Type: | Article |
|---|---|
| Keywords: | Forecasting stock price, Empirical mode decomposition, Intrinsic mode functions, Theta method |
| Taxonomy: | By Subject > Computer & Mathematical Sciences > Statistics |
| Local Content Hub: | Subjects > Computer and Mathematical Sciences |
| Depositing User: | Muslim Ismail @ Ahmad |
| Date Deposited: | 23 Feb 2021 02:58 |
| Last Modified: | 23 Feb 2021 02:58 |
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