Prediction of Gold Future Prices Using CNN-LSTM Model under RNN Online Learning Framework
Gold is a special financial commodity with a safe-haven function.The price of gold futures is affected by many factors and is generally regarded as a non-linear and non-stationary time series,which is difficult to be predicted by traditional forecasting models.We introduce information transmission into traditional online learning algorithms and proposes an online learning algorithm ROA(RNN based Online Algorithm)based on RNN(Recurrent Neural Network).Empirical analysis is conducted using the Chicago Mercantile Exchange gold futures price data,with CNN-LSTM(Convolutional Neural Networks-Long Short Term Memory)as the basic prediction model and MAE(Mean Absolute Error),RMSE(Root Mean Square Error),and R2 as evaluation indicators.The results show that the predictive performance of ROA is superior to traditional online learning algorithms in all evaluation indicators.
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