Bi-GRU-APSO: Bi-Directional Gated Recurrent Unit with Adaptive Particle Swarm Optimization Algorithm for Sales Forecasting in Multi-Channel Retail

Author:

Mogarala Guruvaya Aruna1,Kollu Archana2,Divakarachari Parameshachari Bidare3ORCID,Falkowski-Gilski Przemysław4ORCID,Praveena Hirald Dwaraka5

Affiliation:

1. Department of Artificial Intelligence and Machine Learning, Dayanand Sagar College of Engineering, Bangalore 560078, India

2. Department of Computer Engineering, Pimpri Chinchwad College of Engineering and Research, Pune 412101, India

3. Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru 560064, India

4. Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland

5. Department of Electronics and Communication Engineering, School of Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati 517102, India

Abstract

In the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied customer demands. In order to obtain a better retail sales forecasting, deep learning models are preferred. In this manuscript, an effective Bi-GRU is proposed for accurate sales forecasting related to E-commerce companies. Initially, retail sales data are acquired from two benchmark online datasets: Rossmann dataset and Walmart dataset. From the acquired datasets, the unreliable samples are eliminated by interpolating missing data, outlier’s removal, normalization, and de-normalization. Then, feature engineering is carried out by implementing the Adaptive Particle Swarm Optimization (APSO) algorithm, Recursive Feature Elimination (RFE) technique, and Minimum Redundancy Maximum Relevance (MRMR) technique. Followed by that, the optimized active features from feature engineering are given to the Bi-Directional Gated Recurrent Unit (Bi-GRU) model for precise retail sales forecasting. From the result analysis, it is seen that the proposed Bi-GRU model achieves higher results in terms of an R2 value of 0.98 and 0.99, a Mean Absolute Error (MAE) of 0.05 and 0.07, and a Mean Square Error (MSE) of 0.04 and 0.03 on the Rossmann and Walmart datasets. The proposed method supports the retail sales forecasting by achieving superior results over the conventional models.

Publisher

MDPI AG

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