## 1 Introduction  
Retail sales forecasting is one of the important factors in E-commerce companies due to the economic hardship and strong competition [1]. A good sales forecasting makes an efficient production plan, increases the revenue from sales, and improves the satisfaction of customers. The timely and effective forecasting model is an indispensable and crucial tool for handling the inventory level [2, 3], whereas the inappropriate forecasting model results in insufficient or redundant stock that directly affects the competitive advantage and income [4]. In the recent decades, the researchers have introduced many analytical and statistical regression models for resolving the concerns of retail sales forecasting [5; 6]. The existing models are generally categorized into two types of artificial intelligence models and classical regression models. Some of the classical regression models are exponential smoothing, weighted average, linear regression, moving average, Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), etc. [7; 8]. On the other hand, the common statistical models employed in retail sales forecasting are Seasonal Auto-Regressive Moving Average (SARIMA) and ARIMA [9; 10]. Currently, artificial intelligence models are highly adopted for retail sales forecasting, such as neural networks, fuzzy systems, expert systems, and hybrid models [11; 12]. In artificial intelligence, neural networks are extensively utilized in sales forecasting because of their higher ability and flexible nature in relation to data mining. Additionally, the hybrid models integrate the benefits of dissimilar approaches for improving the performance of forecasting [13; 14]. Currently, most of the existing studies integrate artificial intelligence and classical models to achieve better performance in retail sales forecasting [15]. However, the E-commerce companies face unrivalled issues to ensure precise forecast, which can be noted as (i) complex and dynamic online sale compared to offline sale, (ii) marketing behavior greatly influencing the customers in E-business platforms, and (iii) need to collect an enormous amount of data to enhance the performance of retail sales forecasting. In order to achieve better retail sales forecasting and to reduce the time complexity, an effective automated regression model is implemented in this manuscript. The contributions are listed below:  
* Performed outlier's removal, interpolation of missing data, normalization, and de-normalization in the acquired datasets. The forecasting accuracy is significantly improved by excluding outliers and interpolating missing data. The outlier's removal aims to eliminate rare or unexpected instances in datasets, and a more typical value is interpolated with the missing data to avoid problems like bias and loss of power. Additionally, data integrity is improved by performing normalization and de-normalization using the Z-score normalization technique. * Integrated APSO algorithm, RFE, and MRMR for optimizing features in the acquired Rossmann and Walmart datasets. The feature engineering process minimizes the number of features in the pre-processed datasets and, in turn, decreases the computational complexity and processing time, alongside improving the performance of the regression model. The selection of active features decreases the complexity to linear, and the regression model consumes a minimal processing time of 20.11 s, while consuming 30.12 s in the Rossmann and Walmart datasets. * The selected active features are passed to the Bi-GRU model for precise retail sales forecasting. When related to other regression models, the Bi-GRU model consumes limited memory and is faster in data processing. The effectiveness of the proposed regression model is analyzed based on six evaluation measures: Coefficient of determination (R2), MSE, Normalized Deviation (ND), MAE, Root Mean Square Scale Error (RMSSE), and Normalized Root Mean Square Error (NRMSE). The related papers on the topic \"sales forecasting\" are reviewed in Section 2.