Figure 3
A diagram of the TRNN model structure used for prediction. The diagram illustrates the flow of data through the model. It starts with two input features: historical closing price and trading volume or technical indicators. These inputs feed into the input layer, which has a sequence length of two. The input layer connects to a hidden layer composed of neurons and an activation function. Following the hidden layer is a dropout layer, which helps prevent overfitting. The final output layer predicts the closing price. The diagram shows the sequential flow of data from input features through the layers to the predicted output.Structure of the TRNN model used for prediction