## 2 Literature Survey  
[PERSON] et al. [16] integrated the PSO algorithm with a Long Short-Term Memory (LSTM) network for precise sales forecasting related to the e-commerce companies. Here, the PSO algorithm was implemented for optimizing the number of iterations and the hidden neurons in the LSTM layers. The experiments conducted on the real and online benchmark datasets demonstrated the effectiveness of the presented forecasting model over the nine comparative models. The LSTM network was effective in sales forecasting but required more training samples to learn efficiently. [PERSON] et al. [17] and [PERSON] et al. [18] employed the XGBoost 1.0.0 model for effective sales forecasting by using commodities sales features and data series. Here, sales forecasting was performed for optimizing the inventory management by considering the relevant factors, like transportation time, delivery time, order lead-time, and inventory cost. Generally, the XGBoost model included regularization methods for preventing overfitting, which led to erroneous prediction of new sales data. In addition, the XGBoost model was memory intensive, particularly for large real-time datasets. [PERSON] and [PERSON] [19] adopted an effective learning algorithm for accurate sales forecasting and incorporated Extreme Learning Machine (ELM) and Improved Harmony Search Algorithm (IHSA). The incorporation of ELM and IHSA enhanced the ability of network generalization. The experiments performed on the benchmark and real-time fashion retail datasets revealed that the presented model achieved a superior performance compared to the traditional neural networks. On the other hand, the incorporation of ELM and IHSA with the learning algorithm increased the time complexity of the system. [PERSON] et al. [20] explored the usage of deep learning model (DLM) in the fashion retail sales forecasting by considering a diverse and wide set of data. Here, the DLM performance was compared with other shallow models, and the numerical analysis stated that the DLM obtained results were superior to the shallow models in terms of different evaluation measures. Nonetheless, the DLM was computationally costly and needed a higher-end system to deal with a diverse and wide set of data. [PERSON] [21] combined the SVR model with the variable selection approach for precise sales forecasting. This study forecasted the weekly sales of computer products like display cards, hard disks, main boards, notebooks, and liquid crystal displays. In addition, [PERSON] et al. [22] integrated the LSTM network with an Extreme Deep Factorization Machine (XDeepFM) model for retail sales forecasting by effectively exploring the correlation between the sales factors. The LSTM network was employed for correcting the residuals to enhance the accuracy of the forecasting model. The numerical evaluation proved that the presented forecasting model was superior compared to other traditional forecasting models. As discussed in the earlier literature, the LSTM network was precise in sales forecasting but needed enormous training samples to learn efficiently. [PERSON] et al. [23] integrated a Moth-Flame Optimization (MFO) algorithm with the ELM to predict the transaction volume of e-commerce. The use of a traditional MFO algorithm significantly improved the convergence accuracy but demanded a high execution time. [PERSON] et al. [24] designed a new framework for precisely forecasting the sales of the supply chain, along with the designed framework that incorporated the LSTM network with a light-Gradient Boosting Machine (GBM) model. The designed framework's performance was validated on three supply-chain sales datasets, and the obtained results demonstrated the efficacy of the designed framework over other traditional models by means of accuracy but needed high time complexity. [PERSON] [25] presented an accurate and efficient sales forecasting framework based on the machine-learning models.