-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series …

. This study proposes a novel CNN-LSTM neural network that can extract potential features from sample data to effectively … conventionally used for prediction. In addition, the proposed model is evaluated against SVR and two different LSTM models. The … predictive accuracy of the model. The CNN-LSTM model, with the inclusion of EPU, IDEMV, and both, achieves a high prediction …

through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM … accurate and reliable forecasting in long-memory time series. Additionally, we compare the GARMA-LSTM model with other …

of key landmark models, such as implied volatility, GARCH, LSTM, and Transformer. We open-source our benchmark code to …

scores were integrated into predictive frameworks based on bidirectional long short-term memory (Bi-LSTM) networks and … stagnation phases, with TextBlob demonstrating superior performance to FinBERT in specific scenarios. In addition, Bi-LSTM 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 …