Sales Forecasting Study Based on a Composite Model of Deep Learning and Random Forest

Ziyao Wang () and Yining Liu ()
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Ziyao Wang: Xidian University, School of Economics and Management
Yining Liu: Xidian University, School of Economics and Management

A chapter in Proceedings of the 5th International Conference on Economic Management and Big Data Application (ICEMBDA 2024), 2024, pp 200-206 from Springer

Abstract: Abstract In the current rapidly changing economic market environment, accurately forecasting sales is crucial for optimizing enterprise resources and enhancing market competitiveness. Existing prediction models often fail to fully handle complex data relationships and long-term dependencies, which limits the accuracy and practicality of the forecasts. To address these limitations, this study introduces a composite model that integrates deep learning with Random Forest (RF) to significantly enhance predictive performance. This model employs Convolutional Neural Network (CNN) to capture complex features of time-series data and uses Bidirectional Long Short-Term Memory network (BiLSTM) to manage dependencies in data both before and after, while RF reduce overfitting through multiple decision trees and achieve feature fusion through joint training, thereby optimizing prediction accuracy. Experimental results demonstrate that this model outperforms traditional models on all evaluation metrics, particularly showing exceptional adaptability in highly volatile markets with its accuracy and stability.

Keywords: Market Economy; Sales Forecasting; Big Data Analysis; Deep Learning; Random Forest (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-638-3_20

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DOI: 10.2991/978-94-6463-638-3_20

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