## 4 Experimental Results and Discussions
### Discussion  
Most of the time-series data coming from a variety of application fields are difficult to use to forecast the future. The less stable patterns the data has, the more difficult it is to forecast. The data coming from agricultural areas are known to be the most fluctuating data due to various perturbation factors such as weather, local and global policies, and societal events. These uncertainties make the prediction of agroproduct sales volumes difficult [1]. The sales volume data from a local food shop used in our experiments also reveal high fluctuation, as shown in Figure 1. The large peaks and sudden drops appear in many time steps. All of the four models could not predict the large peaks and sudden drops, as shown in Figure 9. Even human experts cannot predict the sudden changes without prior information on what incurs those changes. To overcome the limitation of the prediction models, we should inject further information from news and SNS into the prediction model. This issue has seldom been studied in the machine learning community, leaving it as a challenging research subject. As the weekly, monthly, and quarterly seasonality of every time step is known in advance, we attempted to explicitly inject the seasonality information into the neural network LSTM model. As expected, this attempt improved the prediction accuracies significantly, as shown in Table 2. The combination of the weekly and monthly information showed the best accuracy in our experiment. The standard LSTM model has the ability to model seasonality. However, its ability is limited to discovering the seasonal patterns in the data and representing the implicit patterns in the model. We argue that the explicit injection of seasonal information improves the accuracy significantly. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Auto\_Arima**} & \multirow{2}{*}{**Prophet**} & \multirow{2}{*}{**LSTM**} & \multicolumn{6}{c}{**SLSTM**} \\ \cline{6-11}  & & & & & \(s^{\text{up}}\) & \(s^{\text{sm}}\) & \(s^{\text{q}}\) & \(s^{\text{mq}}\) & \(s^{\text{mq}}\) & \(s^{\text{mq}}\) & \(s^{\text{mq}}\) \\ \hline \multirow{3}{*}{WO} & MAE & 122.87 & 139.83 & 121.2 & 114.47 & 113.9 & 111.45 & 111.37 & 113.96 & 111.75 & 113.67 \\  & RMSE & 430.72 & 438.1 & 428.6 & 402.59 & 402.22 & 399.56 & 400.76 & 402.22 & 400.62 & 401.67 \\  & NMAE & 0.32 & 0.33 & 0.26 & 0.19 & 0.19 & 0.19 & **0.18** & 0.19 & **0.18** & 0.19 \\ \hline \multirow{3}{*}{Lettuce} & MAE & 50.18 & 49.0 & 45.73 & 33.06 & 38.18 & 38.98 & 32.83 & 36.99 & 34.06 & 33.35 \\  & RMSE & 88.99 & 87.1 & 83.49 & 77.55 & 80.22 & 80.59 & 77.88 & 79.92 & 78.94 & 77.30 \\  & NMAE & 0.28 & 0.31 & 0.29 & **0.19** & 0.24 & 0.24 & **0.19** & 0.23 & 0.20 & 0.20 \\ \hline \multirow{3}{*}{CM} & MAE & 7.94 & 8.3 & 7.5 & 7.31 & 7.77 & 7.74 & 7.44 & 7.82 & 7.43 & 8.27 \\  & RMSE & 12.95 & 13.4 & 12.8 & 12.79 & 13.11 & 12.95 & 13.04 & 12.99 & 12.71 & 13.85 \\  & NMAE & 0.35 & 0.37 & 0.35 & **0.27** & 0.3 & 0.3 & 0.28 & 0.29 & 0.29 & 0.3 \\ \hline \multirow{3}{*}{Onion} & MAE & 233.47 & 233.6 & 237.6 & 208.99 & 225.36 & 235.73 & 196.55 & 212.95 & 190.74 & 192.3 \\  & RMSE & 373.37 & 358.8 & 376.1 & 347.8 & 362.86 & 374.16 & 331.97 & 348.26 & 234.47 & 325.64 \\  & NMAE & 0.29 & 0.29 & 0.31 & 0.25 & 0.28 & 0.29 & 0.25 & 0.27 & **0.24** & 0.25 \\ \hline \multirow{3}{*}{JMT} & MAE & 114.51 & 89.2 & 78.3 & 76.57 & 74.23 & 76.09 & 65.32 & 75.62 & 74.89 & 74.32 \\  & RMSE & 156.06 & 105.8 & 101.8 & 100.81 & 96.09 & 100.05 & 85.75 & 98.7 & 99.69 & 96.53 \\ \cline{1-1}  & NMAE & 0.34 & 0.27 & 0.26 & 0.23 & 0.20 & 0.23 & **0.17** & 0.20 & 0.19 & 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The experimental results for WelshOnion (WO), Lettuce, ChineseMallow (CM), Onion, and JujuubeMiniTomato (JMT). In [4], a new metric measuring the conservation of statistical characteristics was proposed. The metric reflects how well the model predictions conserve the statistical trends of the original data.