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

Authors

  • 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

Keywords:

ARIMA, SARIMA, EMA, ARMA, Moving Mean

Abstract

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.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-02-28

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Volume 2, Issue 1, January-February-2017

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Research Articles

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How to Cite

[1]
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, IInternational 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.