highest accuracy when we used the Distributed Memory model for document embedding and Long Short-Term Memory (LSTM) model for … Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) models. …

the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN …, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo … the original feature set. The results show that LSTM-based models improved using the new approach, even though all models …

-Term Memory (LSTM), and Gated Recurrent Units (GRU), are utilized to assess the similarity between stocks, which is crucial in the … utilizing LSTM models demonstrated the highest performance, achieving an approximate cumulative return of 51.25353%. This …

Rwanda. Specifically, the study employed the long-short-term memory (LSTM) model, a type of recurrent neural network, to …-GARCH models. Notably, the LSTM model yielded lower root mean square error values compared to the ARMA-GARCH models, demonstrating …, specifically Long Short-Term Memory (LSTM), and ARMA-GARCH models. By focusing on five significant currencies traded in the Rwandan …

strategies using MACD, DMI, and KST indicators, and implemented these strategies with LSTM and GRU networks due to their ability … implemented with GRU networks demonstrated superior performance compared to those implemented with LSTM. (3) The intelligent …

This paper studies a long short-term memory (LSTM)-based coherent mortality forecasting method for developing countries … deep neural network model with LSTM architecture to project the life expectancy and lifespan disparity difference, which … countries and three developing regions. The empirical results show that this LSTM-based coherent forecasting method outperforms …