The machine learning and artificial intelligence methods have been found to make this task more efficient and the advent of high throughput computes have proved to be beneficial in these tasks. In this work a hybrid LSTM-GRU network has been used for prediction of the adjusted dosing price of ...
Stochastic gradient descent algorithm was applied to optimize the model performance. CNN-LSTM architecture The growth and development of plants are a dynamic process not only related to spatial, but also associated with temporal information, which are not considered in conventional CNN model. As a ...
workload dataset. Our results show that VTGAN models outperform traditional deep learning and hybrid models, such as LSTM/GRU and CNN-LSTM/GRU, concerning cloud workload prediction and trend classification. Our proposed model records an upward prediction accuracy ranging from95.4%to96.6%. Introduction...
they proposed the bi-directional gate recurrent unit (Bi-GRU) as the classifier model and achieved superior results compared to the traditional machine learning model. In another study, the combination of RoBERTa and LSTM was introduced to learn the long...
RMSE (Root Mean Square Error), MAE (mean absolute error) and R2 (Coefficient of Determination) are employed as evaluation metrics for the model. Experimental results demonstrate that the GRU-Informer outperforms traditional recurrent neural networks like LSTM (Long Short-Term Memory), GRU neural ...
Novel statistical forecasting models for crude oil price, gas price, and interest rate based on meta-heuristic bat algorithm J. Petrol. Sci. Eng., 172 (2019), pp. 13-22 Google Scholar Nguyen and Nabney, 2010 H.T. Nguyen, I.T. Nabney Short-term electricity demand and gas price forecasts...
Design patterns provide a systematic way to convey solutions to recurring modeling challenges. This paper introduces design patterns for hybrid modeling, an approach that combines modeling based on first principles with data-driven modeling techniques. W
stock prediction; stock losing price; neural networks; support vector machines; fully modified Hodrick–Prescott filter; random forest; ARIMA; LSTM; GRU 1. Introduction The stock market is considered one of the most efficient and effective ways to earn passive income. Usually, the closing price ...
Li et al. [20] combined the convolution neural network (CNN), long short-term memory neural network (LSTM), and gated recurrent unit (GRU) algorithm and proposed a prediction model based on deep learning for power load forecasting in Beijing. Massaoudi et al. [21] combined the savitzky ...
To cope with this problem and improve the complex stock market’s prediction accuracy, we propose a new hybrid novel method that is based on a new version of EMD and a deep learning technique known as long-short memory (LSTM) network. The forecasting precision of the proposed hybrid ensemble...