Rethinking Causal and Time Series Models in Health Forecasting : Prospects and Challenges

Authors(6) :-Henry Asante Antwi, Zhou Lulin, Ethel Asante Antwi, Isaac Asare Bediako, Kofi Baah Boamah, Zinet Abdullai

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.

Authors and Affiliations

Henry Asante Antwi
School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China and Institute of Medical Insurance and Healthcare Management, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
Zhou Lulin
School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China and Institute of Medical Insurance and Healthcare Management, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
Ethel Asante Antwi
School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China and School of Graduate Studies, Ghana Technology University, Private Mail Bag, Abeka-Accra, Ghana
Isaac Asare Bediako
School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China
Kofi Baah Boamah
School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China
Zinet Abdullai
School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China

ARIMA, SARIMA, EMA, ARMA, Moving Mean

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Publication Details

Published in : Volume 2 | Issue 1 | January-February 2017
Date of Publication : 2017-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 106-112
Manuscript Number : CSEIT172124
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Henry Asante Antwi, Zhou Lulin, Ethel Asante Antwi, Isaac Asare Bediako, Kofi Baah Boamah, Zinet Abdullai, "Rethinking Causal and Time Series Models in Health Forecasting : Prospects and Challenges", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 1, pp.106-112 , January-February.2017
URL : http://ijsrcseit.com/CSEIT172124

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