Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid-Reference-Cited by-同舟云学术

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Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid

Published:2018-03-03 Issue:10 Volume:32 Page:2849-2859
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Navares Ricardo,Díaz Julio,Linares Cristina,Aznarte José L.

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

http://link.springer.com/article/10.1007/s00477-018-1519-z/fulltext.html

Reference44 articles.

1. Abraham G, Byrnes G, Bain C (2009) Short-term forecasting of emergency inpatient flow. Inf Technol Biomed 13:380–388

2. Alberdi JC, Díaz J, Montero JC, Mirón IJ (1998) Daily mortality in madrid community (spain) 1986–1991: relationship with atmospheric variables. Eur J Epidemiol 14:571–578

3. Anwar M, Lewnard J, Parikh S, Pitzer V (2016) Time series analysis of malaria in afghanistan: using arima models to predict future trends in incidence. Malar J 15:566

4. Aznarte JL, Benítez Sánchez JM, Lugilde DN, de Linares Fernández C, de la Guardia CD, Sánchez FA (2007) Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. Exp Syst Appl 32(4):1218–1225

5. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

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