They illustrate techniques for choosing network structure and inputs, data segmentation, error measures for training, error measures for validation, and ...
Here we evaluate alternative methods for training a network to forecast gold market prices. Essential to this evaluation is the identification of an appropriate ...
In this research, Convolutional Neural Networks and Long Short-Term Memory are used for the experiments to forecast the Gold price movements on the Forex ...
The model combines convolutional neural networks. (CNN) and long short-term memory (LSTM) to improve the trend forecasting of gold prices for better trading ...
Jun 12, 2021 · This research develops a novel model to have a proper estimation of the stock market values with respect to the COVID-19 dataset using long short-term memory ...
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This article build a prediction model based on the historical trading volume of gold and Bitcoin, then establish an investment return maximization model.
Moreover, machine learning techniques like MLP neural networks have broader applications beyond gold price prediction, extending to portfolio optimization, risk.
A BP neural network model based on principal component analysis (PCA) and genetic algorithm (GA) was proposed for the short-term prediction of gold price.
This new framework is based on a technique named variation mode decomposition (VMD) combined with deep learning (DL) and a cumulative sum of squares algorithm.
Then, we built a convolutional neural network (CNN) by taking these cross-batch images as input to learn the features and predict corresponding gold price. Our ...