## 2 Related Work
### Lstm  
In the last decade, because of the development of computing capabilities and the increasing availability of analytical data, various data-analysis techniques have developed rapidly. In particular, models that predict future outcomes based on historical data have been studied and applied in various industries. Predictive models by means of neural networks have shown excellent performance in predicting complex nonlinear time-series data [25], especially when compared to sophisticated physical models [26]. The Recurrent Neural Network (RNN) model has a recursive structure that can transfer the information of the last state to the current state. It works well in time-series data analysis. As shown in the Figure 1, the weights of each node are updated in the course of learning for the hidden layer existing between the input layer and the output layer. The traditional RNN algorithm is effective, but has some limitations. A typical problem is gradient loss, in which a part of the information disappears in each feedback process [27]. As the distance between certain information and the location of the information becomes longer, the gradient gradually decreases in the back-propagation process, and thus the time dependence cannot be stably captured when the model moves out of a certain step. In addition, it has the disadvantage of analysis based on past data while disregarding information contained in a future situation.  
In order to overcome the gradient loss problem of the RNN structure, the proposed network structure is LSTM (Long-Short Term Memory). The LSTM adds a cell state to the hidden layer of the RNN. The structure is shown in the Figure 2. The learning is performed by discarding or updating a value according to the weight by means of the gates included in the cell state. Each cell guarantees the reliability of information transmission and avoids the problem of loss of gradient [28]. The formula of the LSTM structure is as follows.  
\[f_{t} =\sigma(W_{xf}x_{t}+W_{hf}h_{t-1}+b_{f}) \tag{1}\] \[i_{t} =\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+b_{i})\] (2) \[o_{t} =\sigma(W_{xo}x_{t}+W_{ho}h_{t-1}+b_{o})\] (3) \[g_{t} =tanh(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c})\] (4) \[c_{T} =f_{T}\odot c_{t}+i_{t}\odot g_{t}\] (5) \[h_{t} =o_{t}\odot c_{t}tanh(c_{t}) \tag{6}\]  
The most influential factors in predicting inventory demand are sudden changes in situations and events that require real-time response. These factors can be said to have nonlinear properties. Various data analysis techniques have been rapidly developed over the past decade with the development of computing capabilities and the increase of analytical data. In particular, the development of a prediction model that predicts future results based on past data is being studied and applied in various industries. More and more predictive models through neural networks have shown good performance in predicting complex nonlinear time series data, and case studies using linear and nonlinear models using ensemble techniques are also being actively used. Refs. [29; 30] used LSTM to predict the life of the mechanical device, and [31] used an LSTM model to predict power demand  
Figure 1: RNN unit.  
Figure 2: LSTM unit.  
for a short time. Ref. [32] conducted a study on smart metering customers in general homes, and deep-RNN surpassed ARIMA 19.5%, SVR 13.1%, and general RNN 6.5%, in terms of RMSE, compared to the latest techniques of predicting loads in the home.