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A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting
11 references·7 citing·51 second hop
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References

  • 2018Speech emotion recognition using deep 1D & 2D CNN LSTM networks1,044 cited
  • 2020A CNN–LSTM model for gold price time-series forecasting869 cited
  • 2020A CNN-BiLSTM-AM method for stock price prediction633 cited
  • 2021Fake news detection: A hybrid CNN-RNN based deep learning approach567 cited
  • 2017The Role of Big Data and Predictive Analytics in Retailing516 cited
  • 2001A practitioners guide to time-series methods for tourism demand forecasting — a case study of Durban, South Africa250 cited
  • 2020Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail206 cited
  • 2019An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain196 cited
  • 2020Daily retail demand forecasting using machine learning with emphasis on calendric special days176 cited
  • 2022Predictive Analytics for Demand Forecasting – A Comparison of SARIMA and LSTM in Retail SCM147 cited
  • 2018Inventory Management in Supply Chain114 cited

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  • 2025Machine learning in supply chain management: systematic literature review and future research agenda

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40 cited
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