Implementing Method of Ensemble Empirical Mode Decomposition And Recurrent Neural Network For Gold Price Forecasting
Journal Title: International Journal of engineering Research and Applications - Year 2017, Vol 7, Issue 11
Abstract
Gold becomes one of long-term investment options and is used as a value protection against inflation or declining other assets. These gold price fluctuations tend to be nonlinear and uncertain. Most researchers and business practitioners fail to produce consistent pricing analyses, due to the complexity of the dynamic and volatile gold market. One method that can accommodate gold price fluctuations is using Ensemble Empirical Mode Decomposition (EEMD). Furthermore, the results of the gold price analysis can be used in forecasting. Forecasting fluctuations in gold prices are needed by importers, investors, and society to reduce risks and to help in making decision. The forecasting which has been done is the integration between EEMD and Feed-forward Neural Network (FNN) with good forecasting results. However, the use of FNN is less flexible for the use of free parameters, such as the type of activation function, initial initialization, number of input neurons, and output neurons. The setting of flexible free parameters can affect the performance of neural networks and improve forecasting accuracy. One way to overcome the weaknesses of FNN in the use of free parameters is, it can use the Recurrent Neural Network (RNN). The trial in this study is using monthly data of world gold price. The results proves that the performance of EEMD-RNN method forecasting is better than EEMD-FNN.
Authors and Affiliations
Sri Herawati, Firmansyah Adiputra, M. Latif, Aeri Rachmad
Keywords
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- EP ID EP392641
- DOI 10.9790/9622-0711013943.
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