## 4 Performance Analysis  
### Experiment Design  
We first designed a demand-prediction model based on LSTM. Second, we quantify the variability of the data as a function of the 2D kernel mill, and the error range is visualized by calculating the maximum and minimum errors of the prediction result according to the reliability. Last, we propose a demand forecasting model that minimizes the cost by adjusting the predicted amount to reflect the cost incurred if there is insufficient prediction compared to the actual value and the excess cost. We designed an experiment to evaluate the performance of the proposed algorithm. First, the prediction performance of the LSTM is evaluated with respect to the randomly generated periodic-function data. Second, the performance is verified by doing predictions based on actual inventory data. Then, the confidence interval of the prediction result is set up by means of the 2D kernel density-estimation technique proposed in this paper, and the effect is confirmed. Finally, we confirm whether the revised forecast is effective in terms of cost reduction, reflecting the cost of forecast error. The data used in the experiments is divided into randomly generated periodic-function data (Sine Data) and actual enterprise inventory data. First, randomly generated data has 50 values per year, and it is generated as a total of 250 datasets for 5 years. The period of the sine function shape is circular, and the deviation and size increase linearly with the year. The random noise is added to complete the dataset. Four years' worth of data, 80% of all data, are used as learning data, and the remaining 20% of one-year data is used as test data. The data on the demand data are data on one item among the various products of the S company, which is an SME company(plastic injection molding) of the Republic of Korea. The volume of shipments (demand) is organized by week and consists of data from 2016 to September 2019 (3Q). Of the total data, data from 2016 to 2019 (about 83%) will be used for training, and data for the remaining 2020 (until September) will be used for the test. Each experiment is designed to test the validity of the model. For Sine data, 50 test sets are divided into 5 sets of 10 tests to obtain the predicted values for each set, and the error rate is calculated. On the other hand, the warehousing out data is divided into 4 sets of 10 pieces, which are the predicted values for each set, because data for 2020 to be used as a test are not available after September. By means of this validation, the reliability of the experimental results is secured.  
The PC used in the experiment is equipped with Intel R CoreTM i7-8700K CPU @ 3.70 GHz, two-core four-logic processor, and supports 32 GB RAM. Table 1 shows the parameter settings for the LSTM model configuration, and for optimal configuration, loss function evaluated using rmsse and map. The optimizer optimized the model using the adam function, which is commonly used in time series data, and set epoch 1000 times, batch size 4 and verbose 2 to fit the model. Figure 5 shows the Year-wise Box plot and Month-wise Box plot, respectively, to identify the characteristics of the data before time series analysis. In general, trends are identified through the annual Box plot, and Seasonality is identified through the monthly Box plot. In addition, in the case of circularity generally viewed, it reflects repeated but non-periodic fluctuations and usually requires a two-year period, but in this case, it meets the conditions because it is five-year data. When the properties of the data were identified using the Augmented Deckey Fuller Test (ADF Test) for the normality test, 0.23 was derived for the p-value value, which was larger than 0.05, so the normality test was completed. This experiment, which applies various time series analysis models using inventory data, aims to determine whether the desired level of predictive performance can be recorded when learning is performed on actual data.