University of Bahrain
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Enhancing Stock Price Prediction Using Empirical Mode Decomposition, Rolling Forecast and Combining Statistical Methods

Show simple item record Hossain, Mohammad Raquibul Ismail, Mohd Tahir Hossain, Md. Jamal 2022-12-06T20:05:14Z 2022-12-06T20:05:14Z 2022-12-06
dc.identifier.issn 2210-142X
dc.description.abstract Data analytics especially predictive analytics is very important research domain which includes time series forecasting. Nonlinear nonstationary time series are challenging to predict. This paper presents the outcome of the research study in finding better forecasting methods for nonlinear nonstationary time series. Rolling forecast approach and locally adaptive empirical mode decomposition (EMD)-based hybridization were employed with autoregressive integrated moving average (ARIMA) and exponentially weighted moving average (EWMA). Thus, two methods were EMD-ARIMArolling and EMD-EWMArolling of which the later was found better in this study. Also, EMD-EWMArolling was combined with ARIMArolling and EWMArolling using affine combinations to develop affEEArolling and affEEErolling methods. Proposed affEEArolling and affEEErolling along with six other compared methods were employed on nine closing price stock datasets from NASDAQ Financial-100 companies and compared using accuracy measurements. From the results, it was found that proposed methods significantly improved forecast accuracy and outperformed the compared methods (e.g., in ACGL dataset, affEEArolling reduced RMSFE by 55.98% where rolling forecast, EMD-hybridization and affine combination improved 43.7%, 4.24% and 18.28% respectively and affEEErolling improved 56%). Hence, EMD-based hybridizations and forecast combinations can be useful tools for time series forecasting. In addition, such EMD-based advanced methods can be considered for inclusion in financial technologies. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Empirical Mode Decomposition, Intrinsic Mode Functions, ARIMA, EWMA, Stock Price Prediction, Forecast Combination en_US
dc.title Enhancing Stock Price Prediction Using Empirical Mode Decomposition, Rolling Forecast and Combining Statistical Methods en_US
dc.type Article en_US
dc.volume 12 en_US
dc.issue 1 en_US
dc.pagestart 1343 en_US
dc.pageend 1356 en_US
dc.contributor.authoraffiliation School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia en_US
dc.contributor.authoraffiliation Department of Applied mathematics, Noakhali Science and Technology University, Noakhali-3814, Bangladesh en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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