## 2 Related Work  
[PERSON] was the first to introduce a method that was designed specifically for intermittent data [21]. [PERSON] proposed separating the data into two series: one for arrival times and another for positive demand. [PERSON] noted, however, that the time series data for intermittent demand varied significantly from that of conventional time series data. The former have multiple zero-demand intervals that are different from the latter. He presented an alternative technique to forecast demand from intermittent time series data. [PERSON] stated that his method that presupposes independence between demand size and demand intervals. However, [PERSON] et al. [22] casted doubt on this concept of autonomy. This, however, was maintained in subsequent work that improved [PERSON]'s original method, such as [PERSON] and [PERSON] [23]. [PERSON] and [PERSON] investigated [PERSON]'s method and found it to be biased. Later, they presented a modified version of [PERSON]'s method to resolve the bias issue [24]. [PERSON] and [PERSON] proposed a modification of [PERSON]'s technique that attempts to eliminate its inherent bias [8]. Nevertheless, the [PERSON] and [PERSON] method is more biased than the [PERSON] method, particularly for highly intermittent series. [PERSON] et al. identified patterns of intermittent demand in several other scenarios such as heavy machinery, electronics, maritime spare parts, etc. [25]. Similarly, [PERSON] and [PERSON] studied intermittency in automotive spare parts [26]. [PERSON] and [PERSON] analyzed the demand for costly aircraft maintenance parts [1]. They observed that businesses were holding too much inventory due to inaccurate demand forecasts, resulting in subpar service levels. Whether the demand is large or small or arrives at the correct or incorrect time, both can lead to mistakes. Therefore, accurate demand projections are necessary to support inventory holding and replenishment decisions. In addition, as a result of shortened life cycles, technology migrations, time for lengthy production cycles, and protracted lead times for capacity expansion, electronic companies face complexity in inventory management and risk of excess supply and shortfall of important components [27]. The work of [28] performs intermittent prediction on data collected from the Internet of things (IoT) using a recurrent neural network (RNN). The performance evaluation metric is accuracy. In this specific prediction task, RNNs outperformed ANNs. As an alternative to the Croston method, a new method based on stochastic simulation is used to conduct research in [29]. Different evaluation metrics are used such as mean error (ME), mean absolute deviation (MAD), MASE, and D proposed by the authors. In conclusion, the proposed method did not outperform the existing standard. In [30], the ATA technique was compared with the Croston technique. The data from the M4 competition were utilized. The ATA and the exponential smoothing methods are similar, but their respective emphases differ. The study predicted six future time steps. As evaluation metrics, both mean squared error and standardized mean absolute percentage error are utilized. As a metric for out-of-sample evaluation, the ATA method is superior to the Croston method. In [31], a novel method, the modified SBA, is presented for intermittent forecasting. In conclusion, the proposed method can not compete with the current method. The authors of [32] employ a deep neural network (DNN) to predict sensor data. The ARIMA and generalized autoregressive conditional heteroskedasticity (GARCH) techniques have been surpassed by this method. This study utilized simulations that require a well-considered parameter design for this purpose. Ref. [33] provides an example of a simulation design that investigates multiple parameter combinations. The study results demonstrate promising outcomes. A study in [34] introduces a seasonal adjustment method and a dynamic neural network as the primary seasonal forecasting model.