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A Deep Learning Approach For Forecasting Global Commodities Prices, Ahmed Saied Elberawi, Mohamed Belal Prof.
A Deep Learning Approach For Forecasting Global Commodities Prices, Ahmed Saied Elberawi, Mohamed Belal Prof.
Future Computing and Informatics Journal
Forecasting future values of time-series data is a critical task in many disciplines including financial planning and decision-making. Researchers and practitioners in statistics apply traditional statistical methods (such as ARMA, ARIMA, ES, and GARCH) for a long time with varying accuracies. Deep learning provides more sophisticated and non-linear approximation that supersede traditional statistical methods in most cases. Deep learning methods require minimal features engineering compared to other methods; it adopts an end-to-end learning methodology. In addition, it can handle a huge amount of data and variables. Financial time series forecasting poses a challenge due to its high volatility and non-stationarity …