Improving Stock Price Prediction Using Combining Forecasts Methods
This study presents an outcome of pursuing better and effective forecasting methods. The study primarily focuses on the effective use of divide-and-conquer strategy with Empirical Mode Decomposition or briefly EMD algorithm. We used two different statistical methods to forecast the high-frequency EM...
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| 主要な著者: | , , |
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| フォーマット: | 論文 |
| 出版事項: |
Institute of Electrical and Electronics Engineers Inc.
2021
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| オンライン･アクセス: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115674226&doi=10.1109%2fACCESS.2021.3114809&partnerID=40&md5=ad6b6f41c8dfd587325c14a00d3c5c87 http://eprints.utp.edu.my/29423/ |
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| 要約: | This study presents an outcome of pursuing better and effective forecasting methods. The study primarily focuses on the effective use of divide-and-conquer strategy with Empirical Mode Decomposition or briefly EMD algorithm. We used two different statistical methods to forecast the high-frequency EMD components and the low-frequency EMD components. With two statistical forecasting methods, ARIMA (Autoregressive Integrated Moving Average) and EWMA (Exponentially Weighted Moving Average), we investigated two possible and potential hybrid methods: EMD-ARIMA-EWMA, EMD-EWMA-ARIMA based on high and low-frequency components. We experimented with these methods and compared their empirical results with four other forecasting methods using five stock market daily closing prices from the SP/TSX 60 Index of Toronto Stock Exchange. This study found better forecasting accuracy from EMD-ARIMA-EWMA than ARIMA, EWMA base methods and EMD-ARIMA as well as EMD-EWMA hybrid methods. Therefore, we believe frequency-based effective method selection in EMD-based hybridization deserves more research investigation for better forecasting accuracy. © 2013 IEEE. |
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