<ul><li>LSTM models are used for processing sequential data.</li><li>To enhance the performance of LSTM models, multiple inputs can be added.</li><li>LSTM model is designed to learn from patterns within sequential data.</li><li>The multiple inputs are added as a part of a time-step sequence.</li><li>The S&P 500 dataset can be used to create an LSTM model with multiple inputs.</li><li>Multiple inputs help in capturing price swings, market volatility, and offer increased data granularity.</li><li>LSTM models require input in the form [samples, time steps, features].</li><li>The attention mechanism helps the LSTM model focus on the most important parts of a sequence.</li><li>The integration of the attention layer into the LSTM model aids the improved ability to predict trends.</li><li>The LSTM model can be trained using parameters like epochs, batch size, and validation data.</li></ul>

How To Make an LSTM Model with Multiple Inputs?

Discover more