TY  - JOUR
TI  - Rethinking Causal and Time Series Models in Health Forecasting : Prospects and Challenges
AU  - Henry Asante Antwi
AU  -  Zhou Lulin
AU  -  Ethel Asante Antwi
AU  -  Isaac Asare Bediako
AU  -  Kofi Baah Boamah
AU  -   Zinet Abdullai
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2017/02/28
PY  - 2017
DO  - https://doi.org/10.32628/IJSRCSEIT
UR  - https://ijsrcseit.com/CSEIT172124
VL  - 2
IS  - 1
SP  - 106	
EP  - 112 
AB  - Time series and causal models have been longstanding  health forecasting technique applied to both clinical and non-clinical decision making. To date the most common approaches of time series forecasting includes exponential smoothing, ARIMA, SARIMA, Time Series Regression etc. However, like most forecasting models that predates the times series and causal approaches,  the methods have evolved hence the preponderance of many different types of times series and causal models used to aid forecasting. This review, explores the use of time series models in contemporary clinical and non-clinical decision making. It explores the growing interests, challenges and strengths of the ensemble of techniques developed to augmented and consolidate effective medical forecasting in a constantly changing healthcare environment.