combines features from Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) as base learners …

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 …

its occurrence. To build an efficient FDP model, we provide a hybrid model (GALSTM-FDP) that incorporates LSTM and GA … approach predicts distress two years ahead. This research integrates GA with LSTM to find the optimum hyperparameter … configuration for LSTM. Using GA, we focus on optimizing architectural aspects for modeling the optimal network based on prediction …