Business Decision-Making Using Hybrid LSTM for Enhanced Operational Efficiency
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1488-9_12
Reference13 articles.
1. Dave E, Leonardo A, Jeanice M, Hanafiah N (2021) Forecasting Indonesia exports using a hybrid model ARIMA-LSTM. Procedia Computer Sci 179:480–487
2. Christos SC, Panagiotis T, Christos G (2020) Combined multi-layered big data and responsible AI techniques for enhanced decision support in shipping. In: 2020 International conference on decision aid sciences and application, pp 669–673
3. Koo E, Kim G (2022) A hybrid prediction model integrating Garch models with a distribution manipulation strategy based on LSTM networks for stock market volatility. IEEE Access 10:34743–34754
4. Joseph RV, Mohanty A, Tyagi S, Mishra S, Satapathy SK, Mohanty SN (2022) A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting. Comput Electr Eng 103:108358
5. de Oliveira JF, Silva EG, de Mattos Neto PS (2021) A hybrid system based on dynamic selection for time series forecasting. IEEE Trans Neural Networks Learn Syst 33(8):3251–3263
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