Authored by Shelton Peiris*
Abstract
Many data sets from medical science are available as time series, especially the records in births, epidemiology, fatal accidents, medical expenses etc, where the information are collected over time. This paper gives a short review of time series methods which have been used in medical research.
Keywords: Time series; Autoregression; Serial correlation; Stationarity; ARMA; Forecasting
Introduction
A time series is a collection of measurements recorded through a suitable time scale. Although this time scale may not be equally spaced, many applications are based on equally spaced time series data. It is known that time series data are generated when a population or an important phenomenon is monitored over time. Many time series analysts use the family of autoregressive integrated moving average (ARIMA) as it enjoys fruitful applications in modelling and forecasting of time series data (ie. serially or autocorrelated data) arise in almost all social sciences. Each member of this family assumes that future values of the series have a clearly defined dynamic parametric relationship which involves both current and past values together with a random noise. A number of extensions to this ARIMA family have been developed to model time series data not following the standard assumptions. A reason for this is that accurate modelling and analysis are useful in practice to estimate potential future observations or forecast values.
Many countries around the world use time series methodology to increase the quality of human life in health, epidemiology, national planning, controlling mortality etc as they a ect their developments. Planning and forecasting of population or migration are also essential for allocation of funds for social services of nations. Therefore, developing good time series models to best suits data sets and use them for accurate forecasting is essential in applications. This review paper is dedicated and focused on a short review of modeling and forecasting through popular time series models highlighting some potential applications through the R statistical software package.
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