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An Efficient Framework for Predicting Future Retail Sales Using Ensemble DNN-BiLSTM Technique

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Abstract

Forecasting retail sales often requires various number of products from different stores. Existing deep or machine learning techniques fall short of producing accurate classification results because of overfitting and two-class problem that affects the performance of evaluation parameters like precision, recall, accuracy and F-measure. Hence there is a need for an efficient prediction framework that addresses the existing problems. This work proposes an efficient framework for predicting retail sales using an ensemble DNN-BiLSTM framework. We suggest creating a base forecaster pool that includes both individual and pooled forecasting techniques for developing this ensemble approach to forecasting retail sales. Instead of focusing on finding the best individual technique, we suggest finding the optimal combination of forecasts. Classification Accuracy, Precision, Recall, and F-measure performance metrics of the experiment utilizing the proposed ensemble approach DNN + BiLSTM surpass the current DNN, CNN, and LSTM classifiers by 98.3%, 98.1%, 97.8%, and 97.94%, respectively.

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Data Availability

The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors have acknowledged that the REVA University, Bangalore provided the facilities needed to conduct the research.

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  1. School of Computing and Information Technology, REVA University, Bengaluru, India

    K. N. Surendra Babu & Mallikarjun M. Kodabagi

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  1. K. N. Surendra Babu
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Correspondence to K. N. Surendra Babu.

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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

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Babu, K.N.S., Kodabagi, M.M. An Efficient Framework for Predicting Future Retail Sales Using Ensemble DNN-BiLSTM Technique. SN COMPUT. SCI. 5, 150 (2024). https://doi.org/10.1007/s42979-023-02427-3

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