## 2 Literature Survey
At first, the vectors were extracted from the historical sales dataset by conducting feature engineering, and then the XGBoost model was implemented for forecasting the future sales. The experiments performed on the online Walmart retail goods dataset proved that the presented framework obtained a more precise sales prediction than the existing models with limited memory resources and computing time. Nevertheless, the XGBoost model faced two major issues in retail forecasting, namely overfitting and outliers. [PERSON] et al. [26] integrated a random forest with the LSTM network for accurate retail-demand forecasting. The presented framework superiorly analyzed complex relationships between the regression and temporal types that resulted in better prediction than the existing forecasting models. A multivariate real-time dataset was employed for analyzing the performance of the presented framework in light of different measures, such as bias, variance, and prediction accuracy. In the multivariate real-time dataset, the random forest resulted in overfitting with noisy regression tasks, and, additionally, the random forest faced difficulty in handling the categorical variables. [PERSON] and [PERSON] [27] suggested the SVR model for retail sales forecasting on the hierarchical time-series data. Three models were formulated (middle-out, top-down, and bottom-up SVR) for forecasting retail sales related to the travel industry. The SVR model generally underperformed when the number of training samples was lower than the number of features in every data point. [PERSON] et al. [28] integrated eleven forecasting models that comprised DLMs, SVR model, and time-series approaches for effective retail-demand forecasting. In addition, a novel decision strategy was implemented from the inspiration of the ensemble model based on the concept of boosting. The presented system's performance was tested on a real-time dataset and evaluated by means of different error rates. The integration of DLMs, the SVR model, and time-series approaches increased the system complexity, which was one of the main issues in this literature. [PERSON] et al. [29] introduced a new sales prediction framework based on K-Nearest Neighbor (KNN) and linear regression models to formulate commendable decisions and find potential risks in the businesses. The efficacy of the presented framework was tested on an online Rossmann dataset by means of different error rates. As mentioned in the previous literature, the integration of two machine-learning models generally increased the system complexity and processing time. From the overall analysis of the above-stated literature, we noted that the state-of-the-art models ELM, XDeepFM, light GBM, XGBoost, LSTM DLM, SVR, and KNN were applied for precise sales forecasting related to the e-commerce companies. The integration of the above-stated learning models normally maximizes the system complexity and processing time. Furthermore, aggregated forecasting assists strategic decision making on the position which frequently relates to the operational decisions at the store level. These aspects affect the demand and promotional data and increase the substantial complexity, and, subsequently, the forecasters possibly face dimensionality issue of too many constraints and not enough data. To address the aforementioned problems and to achieve better retail sales forecasting, an effective Bi-GRU model is proposed in this manuscript which is clearly described in the following subsections.