A hybrid ARIMA-LSTM model optimized by BP in the forecast of outpatient visits
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s12652-020-02602-x.pdf
Reference32 articles.
1. Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden-Day, San Francisco. https://doi.org/10.1080/00401706.1977.10489562
2. Dechanont S, Jedsadayanmata A, Butthum B, Kongkaew C (2018) Hospital outpatient visits associated with medication-related problems in Thailand: a multicenter prospective observational study. J Patient Saf. https://doi.org/10.1097/PTS.0000000000000367
3. Flores JJ, Rodriguez H, Graff M (2010) Reducing the search space in evolutive design of ARIMA and ANN models for time series prediction. Adv Soft Comput. https://doi.org/10.1007/978-3-642-16773-7_28
4. Gers F, Eck D (2010) Applying LSTM to time series predictable through time-window approaches. Int Conf Artif Neural Netw. https://doi.org/10.1007/3-540-44668-0_93
5. Hadavandi E, Shavandi H, Ghanbari A, Abbasian-Naghneh S (2012) Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals. Appl Soft Comput 12:700–711. https://doi.org/10.1016/j.asoc.2011.09.018
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