-Term Memory (LSTM) for resolving single-period inventory ordering decisions, also termed the Newsvendor Problem (NVP). This study … investigates the performance drivers of hybrid CNN-LSTM architectures, coupled with an evolving algorithm for optimizing network …

) and its variations, in addition to a deep neural network, long short-term memory, known as LSTM. Eight scenarios were …

Conditional Heteroskedasticity) and LSTM (Long Short-Term Memory) models for forecasting the return of ESG volatility and to … ESG index, the GARCH model is more appropriate than the LSTM model. …

, presenting a hybrid technique based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). We have …

-Term Memory (LSTM) and Multiple Linear Regression (MLR) techniques, the study evaluates the effectiveness of this multifaceted … prediction accuracy. Moreover, the utilization of LSTM alongside this comprehensive dataset yields comparable predictive outcomes …

Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price … around the ARMA model of time series and the LSTM algorithm of deep learning. Although it cannot be proved by the Diebold …-Mariano test that the prediction accuracy of random forest regression is significantly better than that of LSTM, the prediction …