(5), an output gate is used to filter out redundant values. The final output is calculated by Equation (6). $W$ is the weight matrix and $b$ refers to the bias.

### Optimization Algorithm and Loss Function

In this section, we will briefly introduce the Optimization algorithm and loss function we used in our prediction model.

-   ***Adadelta Optimization Algorithm***: Apart from Stochastic Gradient Descent, Adaptive Gradient Algorithm or the famous Adam algorithm [@kingma2014adam], we chose Adadelta [@zeiler2012adadelta] as our optimization algorithm. Adadelta is an optimization algorithm that can be used to update network weights iterative based on training data. Adadelta combines the advantages of two other extensions of stochastic gradient descent, Adaptive Gradient Algorithm and Root Mean Square Propagation Algorithm. The method dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla Stochastic Gradient Descent [@zeiler2012adadelta]. The method requires no manual tuning of a learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyperparameters.

-   ***Cross Entropy Loss***: In information theory, the cross entropy between two probability distributions $p$ and $q$ over the same underlying set of events measures the average number of bits 