Forecasting Model Of Forecasting Models

1709 WordsApr 22, 20157 Pages
Forecasting is often defined as the estimation of the value of a variable (or set of variables) at some future point in time (Goodier, 2010). It can be applied to a number of different situations when there is uncertainty about the future and the data collected can aid in decisions that need to be made (Armstrong, 2001). In relation to healthcare, forecasting models have been used to aid their sector’s departments to plan staff rota schedules, ensuring that a sufficient amount of senior staff are available at any given time throughout the day, week, month and year. As explained previously, a fundamental factor that causes overcrowding is a limited supply of resources to treat patients, leading to a longer time spent in an Emergency…show more content…
These models can be characterised as consisting of a time trend, a seasonal factor, a cyclical element and an error term (Kennedy, 2008.) Unlike casual or economic forecasting, where it is assumed there is a historical relationship between a dependent and an independent variable will be consistent in the future, time series models assume the historical components of the model will repeat itself. Research has been undertaken to develop a generalised forecasting model that uses a method that can accurately predict future the attendees and resources needed at Emergency Departments. 1.3.3 Long Range Forecasting for Future Attendees An early attempt to predict attendees was conducted by Milner (1988) who’s study on a single Emergency Department within the UK attempted support to healthcare planning by forecasting annual first, return and total attendances at EDs for Trent districts and the whole of the Trent region. The data of annual first, return and total attendances were collected over a training period of 10 years and evaluated over a period of 1 year using an Autoregressive Integrated Moving Average (ARIMA) method for modelling which falls into time series model category. This method for forecasting this type of data has been supported by other researchers, who state that ARIMA forecasting techniques should be considered for a time series that’s contains a trend or seasonal or non-stationary data. The results
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