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Full-Text Articles in Physical Sciences and Mathematics
Forecasting Economic Growth And Movements With Wavelet Transform And Arima Model, Omar Alsinglawi, Omar Alsinglawi, Mohammad Aladwan, Mohammad Aladwan, Saddam Alwadi, Saddam Alwadi
Forecasting Economic Growth And Movements With Wavelet Transform And Arima Model, Omar Alsinglawi, Omar Alsinglawi, Mohammad Aladwan, Mohammad Aladwan, Saddam Alwadi, Saddam Alwadi
Applied Mathematics & Information Sciences
This study uses historical data and modern statistical models to forecast future Gross Domestic Product (GDP) in Jordan. The Wavelet Transformation model (WT) and Autoregressive Integrated Moving Average (ARIMA) model were applied to the time series data and yielded a best-fitting result of (2,1,1) for estimating GDP between 2022-2031. The study concludes that GDP is expected to increase with a positive growth rate of around 3.22%, and recommends government agencies to monitor GDP, strengthen existing policies, and adopt necessary economic reforms to support growth. Additionally, the private sector is encouraged to enhance production tools to achieve economic growth that benefits …
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
SMU Data Science Review
The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …
Forecasting Anomalous Events And Performance Correlation Analysis In Event Data, Sonya Leech [Thesis]
Forecasting Anomalous Events And Performance Correlation Analysis In Event Data, Sonya Leech [Thesis]
Dissertations
Classical and Deep Learning methods are quite common approaches for anomaly detection. Extensive research has been conducted on single point anomalies. Collective anomalies that occur over a set of two or more durations are less likely to happen by chance than that of a single point anomaly. Being able to observe and predict these anomalous events may reduce the risk of a server’s performance. This paper presents a comparative analysis into time-series forecasting of collective anomalous events using two procedures. One is a classical SARIMA model and the other is a deep learning Long-Short Term Memory (LSTM) model. It then …
Combination Forecasting Of Stock Index Time Series Based On Cooperative Game Theory, Luo Wei
Combination Forecasting Of Stock Index Time Series Based On Cooperative Game Theory, Luo Wei
Journal of System Simulation
Abstract: In view of the characteristics of nonlinear, large amplitude, frequent fluctuations in China's stock market, a prediction method of intelligent composite stock index time series based on the cooperative game is presented. The prediction model of stock index time series is established by using neural network method based on the correlations among the various economic indicators, and the development trend and laws of stock index time series are established by using the improved ARIMA method. The two methods are combined by importing cooperative game method. Simulation results show that the prediction accuracy of the presented method is controlled …