## 2 Related Work
Zero demand is counteracted by preprocessing the initial input data and by adding input nodes to the neural network. This study proposed a revised error measurement method for evaluating performance. The proposed framework for forecasting outperforms competing models for intermittent demand. Two ANN models, each using 36 observations as training data, were proposed in [35]. The proposed model was able to achieve competitive inventory performance relative to Croston despite low forecasting accuracy. In [36], an ANN and RNN are trained and compared for intermittent demand forecasting. The results show that ANN performance is superior to that of RNN in long-term demand series. Another work in [37] conducts an empirical analysis utilizing 5133 SKUs from an airline and acquired forecast performance and inventory performance outcomes for different methodologies. The findings of this study indicate that proposed ANN approaches are less biased as compared to other evaluated methods. Table 1 summarizes different datasets having different patterns including intermittent, lumpy, and smooth. It also shows the industry related to each dataset. The forecasting methods used in the referenced article and its evaluated metrics are also included. From the table, it can be observed that the performance of various methods varies with the input datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Series Type** & **Ref.** & **Application** & **Forecasting Method** & **MAE** & **sMAPE** & **RMSE** & **MAD** & **MASE** \\ \hline \multirow{8}{*}{2021 [38]} & \multirow{8}{*}{Retail industry} & SBA & \(\times\) & \(\times\) & 0.632 & \(\times\) & \(\times\) \\ \cline{3-6}  & & & SES & \(\times\) & \(\times\) & 0.617 & \(\times\) & \(\times\) \\ \cline{3-6}  & & & Croston & \(\times\) & \(\times\) & 0.630 & \(\times\) & \(\times\) \\ \cline{3-6}  & & & Markov-combined & \multirow{2}{*}{0.328} & \multirow{2}{*}{0.406} & \multirow{2}{*}{0.576} & \multirow{2}{*}{\(\times\)} & \multirow{2}{*}{\(\times\)} \\  & & method & & & & \\ \cline{3-6}  & & & ARIMA & 12.39 & \(\times\) & 16.90 & \(\times\) & 1.108 \\ \cline{3-6}  & & & Croston & 13.24 & \(\times\) & 17.31 & \(\times\) & 1.197 \\ \cline{3-6}  & & & SVM & 10.11 & \(\times\) & 16.72 & \(\times\) & 0.901 \\ \cline{3-6}  & & & RNN & 9.29 & \(\times\) & 16.61 & \(\times\) & 0.792 \\ \cline{3-6} Intermittent & & & UNISON data driven & 9.19 & \(\times\) & 15.13 & \(\times\) & 0.768 \\ \cline{3-6}  & & & SVM & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 0.830 \\ \cline{3-6}  & 2021 [39] & Vehicle industry & & ANN & \(\times\) & \(\times\) & \(\times\) & 0.954 \\ \cline{3-6}  & & & RNN & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 0.999 \\ \cline{3-6}  & & & SES & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 1.796 \\ \cline{3-6}  & 2017 [40] & Naval industry & & SBA & \(\times\) & \(\times\) & \(\times\) & 1.678 \\ \cline{3-6}  & & & TSB & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 1.700 \\ \cline{3-6}  & & & Bootstrap & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 1.981 \\ \hline \multirow{8}{*}{LUMPY} & \multirow{8}{*}{2021 [39]} & \multirow{8}{*}{Vehicle industry} & SVM & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 0.821 \\ \cline{3-6}  & & & ANN & \(\times\) & \(\times\) & \(\times\) & 1.042 \\ \cline{3-6}  & & & RNN & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 1.069 \\ \cline{1-1} \cline{3-6}  & & & SES & \(\times\) & \(\times\) & \(\times\) & 9.26 & \(\times\) \\ \cline{1-1} \cline{3-6}  & 2005 [41] & Space industry & & Croston & \(\times\) & \(\times\) & \(\times\) & 8.77 & \(\times\) \\ \cline{1-1} \cline{3-6}  & & & SBA & \(\times\) & 138.36 & \(\times\) & \(\times\) & \(\times\) \\ \cline{1-1} \cline{3-6}  & 2008 [21] & Electronics & & NN & \(\times\) & 128.20 & \(\times\) & \(\times\) & \(\times\) \\ \hline \end{tabular}
\end{table} 
Table 1: Datasets having intermittent lumpy and smooth patterns, their forecasting methods, and accuracies on different metrics.