Опубликована: Дек. 12, 2023

The amazing capacity of Long Short-Term Memory (LSTM) networks to record complex temporal connections in sequential data has drawn a lot attention recent years. In the context stock market prediction, field where precise price movement forecasting is crucial, this study investigates use LSTM networks. We explore basic ideas behind and explain how they may be used for time series because their ability analyze understand data. first section gives brief introduction architecture its benefits over more conventional models. talk about important feature engineering preparation are improving LSTMs' predictive power forecasting. also model assessment methods hyperparameter adjustment maximize performance. study, we investigate several approaches, such as single-step multi-step forecasting, using prediction. look at external influences affect performance model, including sentiment analysis news economic indicators. discuss potential remedies problems overfitting scarcity that inherent LSTM-based

Язык: Английский