What is traditional gold price forecasting methods?
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Traditional gold price forecasting methods include linear relationship models and simple moving averages (SMA), weighted moving averages (WMA), exponential moving averages (EMA), and autoregressive integrated moving averages (ARIMA). These methods often struggle to capture the complex patterns and dynamics of gold prices due to their linear nature and limited feature consideration. To enhance forecasting accuracy, newer approaches have been developed, such as incorporating association rules and long short-term memory (LSTM) models, which offer a nonlinear-based method for predicting gold prices with lower mean absolute percentage error (MAPE) metrics. Additionally, the use of Dilated Convolution Long Short-Term Memory (DCLSTM) models, which combine Convolution Neural Network (CNN) and LSTM, have shown improved prediction accuracy compared to traditional methods like ARIMA, K-Nearest Neighbor (KNN), and Back Propagation Neural Network (BPNN).
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| Papers (5) | Insight |
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28 Dec 2018 2 Citations | Traditional gold price forecasting methods are insufficient due to gold's nonlinearity and high noise. The ESMD multi-frequency combination model proposed in the paper offers a more accurate prediction approach. |
| Traditional gold price forecasting methods include Autoregressive Integrated Moving Average (ARIMA), K-Nearest Neighbor (KNN), and Back Propagation Neural Network (BPNN), which are less reliable compared to the DCLSTM model proposed in the research. | |
| Traditional gold price forecasting methods like ARIMA, moving averages, and exponential smoothing are not specifically mentioned in the paper. | |
| Traditional gold price forecasting methods like ARIMA, moving averages, and exponential smoothing are not specifically mentioned in the paper. | |
| Traditional gold price forecasting methods include linear models like SMA, WMA, EMA, and ARIMA. This study introduces a novel approach using LSTM and association rules for more accurate predictions. |
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