首页|New Machine Learning Study Findings Reported from Shanghai Normal University (Re alized Volatility Forecasting for Stocks and Futures Indices With Rolling Ceemda n and Machine Learning Models)
New Machine Learning Study Findings Reported from Shanghai Normal University (Re alized Volatility Forecasting for Stocks and Futures Indices With Rolling Ceemda n and Machine Learning Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators discuss new findings in Machine Learning. According to news reportingout of Shanghai, People's Republic of China, by NewsRx editors, research stated, "As an essential indexfor measur ing market risk, realized volatility (RV) possesses mixed features and volatilit y aggregation,which makes it difficult for machine learning (ML) models to iden tify its features and trends directly foraccurate prediction. Hence, this study first uses the rolling CEEMDAN (complete ensemble empirical modedecomposition with adaptive noise) method to decompose the original RV sequence of the major s tockmarket indices as well as the bean and the metal futures indices."Financial support for this research came from Shanghai Planning Project of Philo sophy and SocialScience.
ShanghaiPeople's Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningShanghai Normal University
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