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https://doi.org/10.62836/jcmea.v3i1.030109
Yunxiang Gan, Jiahuai Ma, & Kaixian Xu. (2023). Enhanced E-Commerce Sales Forecasting Using EEMD-Integrated LSTM Deep Learning Model. Journal of Computational Methods in Engineering Applications, 3(1), 1–11. https://doi.org/10.62836/jcmea.v3i1.030109
Volume 3, Issue 1, 2023

Enhanced E-Commerce Sales Forecasting Using EEMD-Integrated LSTM Deep Learning Model

E-commerce sales data often exhibit complex time series characteristics and are influenced by multiple factors, making traditional forecasting methods inadequate in capturing these dynamics. To address these challenges, this paper presents a forecasting model that integrates Ensemble Empirical Mode Decomposition (EEMD) with Long Short-Term Memory (LSTM) networks. The model first applies EEMD to decompose the original data signal into multiple Intrinsic Mode Function (IMF) components. These components, along with the original data, are then fed into the LSTM network for predictive analysis. As a case study, the proposed model is tested using a sales dataset of an Amazon clothing product. The results demonstrate that the model achieves a forecasting accuracy of 91%, surpassing several commonly used forecasting approaches in precision and reliability. This study highlights the potential of the EEMD-LSTM approach in improving sales forecasts for e-commerce platforms.

forecasting model; e-commerce sales data; ensemble empirical mode decomposition; deep learning

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Supporting Agencies

  1. Funding: Not applicable.